{
  "meta": {
    "attribution": "Anchor Terminal (https://www.anchorterminal.com)",
    "docs": "https://www.anchorterminal.com/docs/",
    "generatedAt": "2026-10-04",
    "license": "CC-BY-4.0",
    "method": "https://www.anchorterminal.com/benchmark/",
    "methodology": "0.3",
    "openapi": "https://www.anchorterminal.com/openapi.json",
    "preview": false,
    "run": "2026-10-01",
    "runLabel": "October 2026 research run"
  },
  "tool": {
    "category": "accounting",
    "endpoint": "https://ainetcafe.com/mcp?s=registry",
    "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ainetcafe-ai-netcafe.json",
    "kind": "mcp",
    "listed": "indexed",
    "liveUrl": "https://www.anchorterminal.com/api/v1/live/ainetcafe-ai-netcafe.json",
    "markdownUrl": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe.md",
    "mcpTools": {
      "check": {
        "checker": "anchor-check/1.0",
        "totalTokens": 7810,
        "counts": {
          "error": 0,
          "note": 0,
          "warn": 2
        },
        "findings": [
          {
            "rule": "TC14",
            "severity": "warn",
            "tool": "create_task",
            "message": "allowed values are in the description, not an enum: kind",
            "fix": "Move them into enum."
          },
          {
            "rule": "TC18",
            "severity": "warn",
            "tool": "merge_tables",
            "message": "readOnlyHint is true but the name says \"merge\"",
            "fix": "If it changes anything, readOnlyHint must be false."
          }
        ]
      },
      "checkedAt": "2026-10-04T22:22:03Z",
      "count": 34,
      "note": "answered without the initialize handshake",
      "schemaTokens": 7810,
      "status": "ok",
      "tools": [
        {
          "name": "what_can_you_do",
          "title": "Find the right tool for a task",
          "description": "Describe a task in plain language (any language) and get back exactly which tools on this server do it, with ready-to-run example calls — instead of reading the whole catalogue and guessing. Also returns multi-step recipes when a task needs several tools chained (invoices to a ledger, a bank statement reconciled, a messy CSV turned into a deliverable). Deterministic and free: it calls no model, costs nothing, and never runs out of quota. Call this FIRST when you are not sure what this server offers.",
          "inputSchema": {
            "properties": {
              "task": {
                "description": "What you are trying to do, e.g. \"reconcile a bank statement against my books\" or \"把一堆发票整理成能入账的表格\"",
                "type": "string"
              }
            },
            "required": [
              "task"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "list_apps",
          "title": "List hosted open-source AI applications",
          "description": "List the open-source AI applications hosted and ready to run at AI NetCafé (ainetcafe.com). Each one normally requires local setup (Docker/Python + your own model API key); here they run pre-configured. Use this to find a tool for a task like translating a PDF with formulas intact, generating a PowerPoint file, polishing an academic paper, or running an autonomous research report. Do not call this first when the request already clearly matches compare_models, translate_pdf, deep_research, or make_slides; call that task tool directly. Example — GET https://ainetcafe.com/t/list_apps",
          "inputSchema": {
            "properties": {
              "category": {
                "description": "Optional filter, e.g. \"office\", \"research\", \"chat\".",
                "type": "string"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "apps": {
                "items": {
                  "type": "object"
                },
                "type": "array"
              },
              "try_in_browser": {
                "type": "string"
              }
            },
            "required": [
              "apps"
            ],
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "get_app",
          "title": "Get details of one application",
          "description": "Full details of one hosted application: what it does, how to use it, measured benchmark scores, source repository, and the URL a human can open to run it. Example — GET https://ainetcafe.com/t/get_app?slug=\u003cslug-from-list_apps\u003e",
          "inputSchema": {
            "properties": {
              "slug": {
                "description": "Application slug, from list_apps.",
                "type": "string"
              }
            },
            "required": [
              "slug"
            ],
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "name": {
                "type": "string"
              },
              "open_url": {
                "type": "string"
              },
              "slug": {
                "type": "string"
              }
            },
            "required": [
              "slug",
              "name"
            ],
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "ask_model",
          "title": "Run a prompt on a specific LLM",
          "description": "Send a prompt to one specific large language model and get the answer plus measured platform cost metadata. The beta platform covers the user charge ($0.00); capacity limits still apply. Example — GET https://ainetcafe.com/t/ask_model?prompt=Say+hi\u0026model=deepseek-v4-flash",
          "inputSchema": {
            "properties": {
              "max_tokens": {
                "description": "Optional output cap.",
                "type": "integer"
              },
              "model": {
                "description": "Model id. Call list_models for available ids. Defaults to a cheap capable model.",
                "type": "string"
              },
              "prompt": {
                "description": "The prompt to send.",
                "type": "string"
              },
              "system": {
                "description": "Optional system instruction.",
                "type": "string"
              }
            },
            "required": [
              "prompt"
            ],
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "answer": {
                "type": "string"
              },
              "cost_usd": {
                "type": "number"
              },
              "latency_ms": {
                "type": "number"
              },
              "model": {
                "type": "string"
              }
            },
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": false,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        },
        {
          "name": "compare_models",
          "title": "Run the same prompt on several models and compare",
          "description": "Run one prompt across multiple LLMs in parallel and return every answer side by side with measured platform cost metadata and latency. The beta platform covers the user charge ($0.00). This answers \"which model should I actually use for this kind of task?\" with data instead of guesswork. Example — GET https://ainetcafe.com/t/compare_models?prompt=Explain+CAP+theorem+in+1+line",
          "inputSchema": {
            "properties": {
              "models": {
                "description": "Model ids to compare (2-5). Defaults to a cheap/mid/strong spread.",
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              "prompt": {
                "description": "The prompt to send to every model.",
                "type": "string"
              },
              "system": {
                "description": "Optional system instruction applied to all.",
                "type": "string"
              }
            },
            "required": [
              "prompt"
            ],
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "results": {
                "items": {
                  "type": "object"
                },
                "type": "array"
              },
              "summary": {
                "type": [
                  "object",
                  "null"
                ]
              }
            },
            "required": [
              "results"
            ],
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": false,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        },
        {
          "name": "list_models",
          "title": "List available models and capacity",
          "description": "List every model currently available in the free beta with reference input/output rates and health metadata. Those rates are platform cost metadata only; every user charge is $0.00 during the beta. Example — GET https://ainetcafe.com/t/list_models",
          "inputSchema": {
            "properties": {
              "tier": {
                "description": "Optional reference tier filter. All currently healthy tiers are available without a user key during the beta.",
                "enum": [
                  "free",
                  "premium"
                ],
                "type": "string"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "models": {
                "items": {
                  "type": "object"
                },
                "type": "array"
              }
            },
            "required": [
              "models"
            ],
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "remember",
          "title": "Store a memory (persists across sessions within your workspace)",
          "description": "Persist a durable memory: an architecture decision, a stable user preference, a verified bug fix, or an important discovery. The free beta provides a bounded per-caller/workspace memory pool; no personal API key is required. Do not store secrets or raw logs. Example — tools/call remember {\"content\":\"Deploy key rotates monthly\"}",
          "inputSchema": {
            "properties": {
              "content": {
                "description": "The memory itself, self-contained (≤2000 chars).",
                "type": "string"
              },
              "kind": {
                "description": "Category; default \"note\".",
                "enum": [
                  "decision",
                  "preference",
                  "bugfix",
                  "discovery",
                  "note"
                ],
                "type": "string"
              },
              "project": {
                "description": "Optional project name to scope recall later.",
                "type": "string"
              }
            },
            "required": [
              "content"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": false,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        },
        {
          "name": "recall",
          "title": "Recall stored memories",
          "description": "Retrieve previously stored memories, optionally filtered by search query and/or project. Call at the start of work on a known project to restore context: why decisions were made, known fixes, preferences. Example — GET https://ainetcafe.com/t/recall?query=\u003cwhat+to+remember\u003e  (needs a workspace/key for durable memory)",
          "inputSchema": {
            "properties": {
              "limit": {
                "description": "Max results (default 8, up to 20).",
                "type": "integer"
              },
              "project": {
                "description": "Optional project filter.",
                "type": "string"
              },
              "query": {
                "description": "Optional search terms; omit to list the most recent.",
                "type": "string"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "web_search",
          "title": "Search the web (meta-search)",
          "description": "Search the live web through a self-hosted SearXNG meta-search (aggregates dozens of engines, no tracking). Returns titles, URLs and snippets. Use when you need current information or sources. Example — GET https://ainetcafe.com/t/web_search?query=latest+MCP+spec",
          "inputSchema": {
            "properties": {
              "max_results": {
                "description": "Max results (default 8, up to 20).",
                "type": "integer"
              },
              "query": {
                "description": "The search query.",
                "type": "string"
              }
            },
            "required": [
              "query"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "fetch_page",
          "title": "Fetch a web page as clean Markdown",
          "description": "Fetch a public URL and return clean LLM-ready Markdown from the server-rendered response. This tool does not execute browser JavaScript; for SPA or empty-text pages, use web_search, a browser, or the site's API. Use it after web_search to read a reachable public source, or to ingest a static page for analysis. Example — GET https://ainetcafe.com/t/fetch_page?url=https://example.com",
          "inputSchema": {
            "properties": {
              "url": {
                "description": "The page URL to fetch.",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "model_costs",
          "title": "Measured platform cost across models",
          "description": "Measured platform cost metadata for one call on each model; your charge is $0.00 during the free beta. Vendors publish per-million-token list prices, but a call's cost depends on how many tokens the model chooses to emit — models differ by an order of magnitude on the same prompt. standard_bench sends an IDENTICAL prompt to every model, so the difference is the model, not the workload — use that to choose a model before bulk work. production_mixed is real traffic and is NOT comparable across models. Free to cite, CC BY 4.0. Example — GET https://ainetcafe.com/t/model_costs",
          "inputSchema": {
            "properties": {
              "days": {
                "description": "Measurement window in days (default 30).",
                "type": "integer"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "ai_visibility",
          "title": "Can AI assistants read and cite this site?",
          "description": "Audit a URL for AI visibility: which AI crawlers robots.txt actually allows (parsed per user-agent group, not keyword-matched), whether llms.txt / sitemap / JSON-LD / canonical exist, and how much real text an agent gets without running JavaScript. Returns a score plus the specific fixes, ordered by impact.",
          "inputSchema": {
            "properties": {
              "url": {
                "description": "Page to audit, e.g. https://example.com",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "pdf_to_markdown",
          "title": "PDF or scanned page → structured Markdown",
          "description": "Convert a PDF (or a scanned page image) into clean Markdown that keeps headings, lists and tables, and puts multi-column pages in the right reading order. Text-layer PDFs are read exactly and cost far less; images go through a vision model.",
          "inputSchema": {
            "properties": {
              "url": {
                "description": "Public URL of the PDF, or of a page image (png/jpg) for scanned documents.",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "extract_tables",
          "title": "PDF tables → structured rows (with schema alignment)",
          "description": "Extract tables from a PDF into structured rows (JSON + CSV). Pass fields to force a fixed set of columns — that aligns a pile of documents that each name their headers differently into one consistent table. Rows the model was unsure about are flagged rather than guessed. Text-layer PDFs only.",
          "inputSchema": {
            "properties": {
              "fields": {
                "description": "Optional comma-separated target columns, e.g. \"invoice_no,supplier,date,amount\". Omit to infer from the header.",
                "type": "string"
              },
              "url": {
                "description": "Public URL of the PDF.",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "extract_statement",
          "title": "Bank statement PDF → transactions + reconciliation check",
          "description": "Turn a bank statement or transaction PDF into a clean transaction table (JSON + CSV), then cross-check it: opening + credits - debits must equal the stated closing balance. If it does not balance you get the exact difference and which row the running balance first breaks at — so you know whether the table is safe to use for accounting. Text-layer PDFs only (scanned images not yet supported).",
          "inputSchema": {
            "properties": {
              "url": {
                "description": "Public URL of the statement PDF.",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "json_yaml",
          "title": "JSON ↔ YAML, either direction, auto-detected",
          "description": "Converts JSON to YAML or YAML to JSON. It works out which one you gave it, so you do not have to say. A parse failure comes back with the parser message instead of silently producing something that looks fine and is not. Use when a config, a CI file, or a Kubernetes manifest needs to be in the other format.",
          "inputSchema": {
            "properties": {
              "text": {
                "description": "The JSON or YAML content.",
                "type": "string"
              },
              "to": {
                "description": "Optional: \"json\" or \"yaml\" to force the direction.",
                "type": "string"
              }
            },
            "required": [
              "text"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "validate_json",
          "title": "Is this JSON valid — and does it have the keys you need?",
          "description": "Checks that text parses as JSON, and optionally that required keys are present with the right top-level types. Returns the specific violations, not just true/false. Checks required + types only — not full JSON Schema, and it says so rather than pretending. Use before feeding generated JSON into something that will fail on it.",
          "inputSchema": {
            "properties": {
              "schema": {
                "description": "Optional JSON Schema (as JSON text) — required[] and properties[].type are checked.",
                "type": "string"
              },
              "text": {
                "description": "The JSON to validate.",
                "type": "string"
              }
            },
            "required": [
              "text"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "diff_text",
          "title": "What changed between two texts, line by line",
          "description": "Returns which lines were added and which were removed, with line numbers — computed with a longest-common-subsequence, not guessed by a model. Use to compare two versions of a config, a document, or any command output, instead of asking an LLM to eyeball two blobs and hoping it notices.",
          "inputSchema": {
            "properties": {
              "a": {
                "description": "The first (before) text.",
                "type": "string"
              },
              "b": {
                "description": "The second (after) text.",
                "type": "string"
              }
            },
            "required": [
              "a",
              "b"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "jwt_decode",
          "title": "See inside a JWT — header, payload, and whether it has expired",
          "description": "Decodes the header and payload of a JWT and reports issued-at / expiry as readable timestamps plus seconds remaining. The signature is NOT verified and the response says so — decoding is fine for debugging a token you already hold, but never treat these values as proof of anything; verification needs the secret and belongs in your own service.",
          "inputSchema": {
            "properties": {
              "token": {
                "description": "The JWT string.",
                "type": "string"
              }
            },
            "required": [
              "token"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "regex_test",
          "title": "Does this regex match — and what does it capture?",
          "description": "Runs a regular expression against sample text and returns every match with its position and capture groups (named groups included). Use before wiring a pattern into code, instead of guessing whether the escaping survived the trip through JSON and the shell.",
          "inputSchema": {
            "properties": {
              "flags": {
                "description": "Optional flags, e.g. \"gi\". Default \"g\".",
                "type": "string"
              },
              "pattern": {
                "description": "The regular expression, without surrounding slashes.",
                "type": "string"
              },
              "text": {
                "description": "The text to test against.",
                "type": "string"
              }
            },
            "required": [
              "pattern",
              "text"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "diff_tables",
          "title": "Two tables → what differs (the VLOOKUP job, no amounts needed)",
          "description": "Matches rows across two CSVs on a key column and reports three things: keys only in A, keys only in B, and keys in both whose other columns disagree — naming the exact column and both values. Unlike reconcile_ledger this needs no amount column, so it also fits name lists, inventory counts, permission tables, and any \"these two exports should match\" check.",
          "inputSchema": {
            "properties": {
              "key": {
                "description": "Column that identifies a row, e.g. id.",
                "type": "string"
              },
              "text_a": {
                "description": "Or the first CSV content directly.",
                "type": "string"
              },
              "text_b": {
                "description": "Or the second CSV content directly.",
                "type": "string"
              },
              "url_a": {
                "description": "Link to the first CSV.",
                "type": "string"
              },
              "url_b": {
                "description": "Link to the second CSV.",
                "type": "string"
              }
            },
            "required": [
              "key"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "clean_table",
          "title": "Messy CSV → tidy CSV, with a report of every change",
          "description": "Tidies a spreadsheet export: removes duplicate rows, trims whitespace (half-width and full-width — Chinese exports are full of 　), unifies the half-dozen ways a cell can say \"empty\" (NA / null / - / 无), drops empty rows and columns, and can split one column into several. Returns the cleaned CSV plus exactly what changed: rows in, rows out, duplicates removed, cells trimmed per column. It can also transpose rows/columns and unpivot a wide table into a long one. The row arithmetic is verified in code — if in − removed ≠ out, the response says so instead of handing back a table nobody can check. Use when a CSV came out of Excel or an export and needs cleaning before analysis.",
          "inputSchema": {
            "properties": {
              "keep": {
                "description": "For wide_to_long: comma-separated id columns to keep as-is. Defaults to the first column.",
                "type": "string"
              },
              "ops": {
                "description": "Comma-separated, default \"dedupe,trim,drop_empty,unify_blank\". Also available: split_column, transpose (swap rows/columns), wide_to_long (unpivot a wide table into the long format analysis tools expect).",
                "type": "string"
              },
              "split_by": {
                "description": "Separator to split on, default a single space.",
                "type": "string"
              },
              "split_column": {
                "description": "Column name to split (requires ops to include split_column).",
                "type": "string"
              },
              "text": {
                "description": "The CSV content itself. Provide this or url.",
                "type": "string"
              },
              "url": {
                "description": "Link to the CSV. Provide this or text.",
                "type": "string"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "merge_tables",
          "title": "Several CSVs → one, columns unioned, row counts proven",
          "description": "Combines up to 20 CSVs into a single table. Headers do not have to match: columns are unioned and a file missing a column contributes blanks for it, so rows never shift silently — the failure mode that makes hand-merged spreadsheets untrustworthy. Reports each source file row count and checks in code that they sum to the merged total. Use for monthly exports, per-store sheets, or any set of files with the same subject but drifting headers.",
          "inputSchema": {
            "properties": {
              "texts": {
                "description": "Or pass the CSV contents directly as an array.",
                "items": {
                  "type": "string"
                },
                "type": "array"
              },
              "urls": {
                "description": "Comma-separated CSV links, at least two.",
                "type": "string"
              }
            },
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "reconcile_ledger",
          "title": "Two tables → what does not match (the VLOOKUP job), with the arithmetic proof",
          "description": "Reconciles two sets of records — your books against a bank, platform, or supplier statement. Matches rows on a key column, compares an amount column, and returns three lists: only in A, only in B, and same key but different amount. Amounts are compared in integer cents, so 0.1 + 0.2 never invents a phantom difference for someone to chase. The response also proves the result: the listed differences are re-added and must equal the gap between the two totals, checked in code. Use for month-end close, platform payouts vs orders, or any \"these two numbers should agree and do not\" problem. This is the job people do by hand with VLOOKUP or a groupby and then cannot prove they got right.",
          "inputSchema": {
            "properties": {
              "amount": {
                "description": "Numeric column to compare, e.g. amount.",
                "type": "string"
              },
              "key": {
                "description": "Column name to match rows on, e.g. order_id.",
                "type": "string"
              },
              "text_a": {
                "description": "Or the CSV content of side A directly.",
                "type": "string"
              },
              "text_b": {
                "description": "Or the CSV content of side B directly.",
                "type": "string"
              },
              "url_a": {
                "description": "Link to side A (e.g. your books).",
                "type": "string"
              },
              "url_b": {
                "description": "Link to side B (e.g. the statement).",
                "type": "string"
              }
            },
            "required": [
              "key",
              "amount"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "extract_invoices",
          "title": "A batch of invoices → one ledger-ready table (arithmetic-checked)",
          "description": "Give it up to 20 invoice URLs (PDF or page images) and get back one table ready to post: number, date, seller, buyer, net / tax / gross, currency. Every row is checked in code — net + tax must equal gross — and the batch total is re-added independently, so a row the model misread is flagged with the exact difference instead of quietly landing in your books. Mixed currencies get no batch total on purpose: adding them together would be an accounting error. CSV is UTF-8 with BOM so Excel opens it right.",
          "inputSchema": {
            "properties": {
              "urls": {
                "description": "Invoice URLs — comma-separated, or pass an array. Up to 20 per call.",
                "type": "string"
              }
            },
            "required": [
              "urls"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "create_task",
          "title": "Schedule a recurring task that runs on our servers",
          "description": "Create a task that runs on a schedule in our cloud — you do not keep anything running. It only notifies you when the result actually changes. Kinds: watch_page (Watch a web page and report when its content changes); daily_answer (Re-run a web-researched question on a schedule and report when the answer changes); watch_reachability (Track whether a site stays reachable from mainland China); pipeline (Run one of your production lines (create_pipeline) on a schedule; every run leaves a proof-carrying work order). Needs a workspace token (?w=ws_... on your MCP URL) so you can manage it later. Application and model calls are subsidized during the free beta; your charge is $0.00 and capacity limits apply.",
          "inputSchema": {
            "properties": {
              "input": {
                "description": "The URL to watch, or the question to re-research.",
                "type": "string"
              },
              "interval_seconds": {
                "description": "How often to run. Minimum 900 (15 min), default 3600.",
                "type": "integer"
              },
              "kind": {
                "description": "watch_page | daily_answer | watch_reachability | pipeline",
                "type": "string"
              },
              "notify_url": {
                "description": "Optional https webhook to POST results to when they change.",
                "type": "string"
              }
            },
            "required": [
              "kind",
              "input"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": false,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        },
        {
          "name": "list_tasks",
          "title": "List your scheduled tasks",
          "description": "Show scheduled tasks, next run times, run counts, and measured platform cost metadata. User charge is $0.00 during the beta.",
          "inputSchema": {
            "properties": {},
            "required": [],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "get_task_runs",
          "title": "See what a scheduled task has produced",
          "description": "Recent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.",
          "inputSchema": {
            "properties": {
              "limit": {
                "description": "How many recent runs, max 20, default 5.",
                "type": "integer"
              },
              "task_id": {
                "description": "From create_task or list_tasks.",
                "type": "integer"
              }
            },
            "required": [
              "task_id"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "delete_task",
          "title": "Delete a scheduled task",
          "description": "Stop and remove a scheduled task and its run history.",
          "inputSchema": {
            "properties": {
              "task_id": {
                "description": "From list_tasks.",
                "type": "integer"
              }
            },
            "required": [
              "task_id"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": true,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        },
        {
          "name": "transpile_sql",
          "title": "Translate SQL between dialects",
          "description": "Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.",
          "inputSchema": {
            "properties": {
              "read": {
                "description": "Source dialect, e.g. \"mysql\". Omit to auto-detect from generic SQL.",
                "type": "string"
              },
              "sql": {
                "description": "The SQL statement (or several, separated by semicolons).",
                "type": "string"
              },
              "write": {
                "description": "Target dialect, e.g. \"postgres\", \"bigquery\", \"doris\".",
                "type": "string"
              }
            },
            "required": [
              "sql",
              "write"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "china_reachability",
          "title": "Test if a URL is reachable from mainland China",
          "description": "Fetch a URL from a real mainland-China network egress and report HTTP status, latency and China DNS resolution. Answers \"is my site/API usable from China?\" with a measurement instead of a guess — you cannot get this from a VPS abroad.",
          "inputSchema": {
            "properties": {
              "url": {
                "description": "Full URL to test, e.g. https://example.com",
                "type": "string"
              }
            },
            "required": [
              "url"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "render_diagram",
          "title": "Render a diagram from text",
          "description": "Turn diagram-as-code into an image: Mermaid, PlantUML, Graphviz/DOT, C4, Excalidraw and 20+ more (self-hosted Kroki). Returns a hosted SVG/PNG URL you can embed directly in Markdown or HTML. Example — GET \"https://ainetcafe.com/t/render_diagram?source=graph TD;A--%3EB\u0026format=png\"",
          "inputSchema": {
            "properties": {
              "format": {
                "description": "\"svg\" (default) or \"png\".",
                "type": "string"
              },
              "source": {
                "description": "The diagram source code (e.g. a Mermaid flowchart).",
                "type": "string"
              },
              "type": {
                "description": "Diagram language: mermaid (default), plantuml, graphviz, c4plantuml, excalidraw, blockdiag, erd…",
                "type": "string"
              }
            },
            "required": [
              "source"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "check_job",
          "title": "Check a long-running job",
          "description": "Get the status or result of a job started by deep_research, translate_pdf, or make_slides. Poll every 15-30 seconds until status is \"done\" or \"error\". While work is pending, follow retry_after_seconds and next_action; when complete, prefer structured_result when present. Example — GET https://ainetcafe.com/t/check_job?job_id=\u003cid-from-a-job-tool\u003e",
          "inputSchema": {
            "properties": {
              "job_id": {
                "description": "The job_id returned when the task was started.",
                "type": "string"
              }
            },
            "required": [
              "job_id"
            ],
            "type": "object"
          },
          "outputSchema": {
            "properties": {
              "error": {
                "type": "string"
              },
              "is_terminal": {
                "type": "boolean"
              },
              "job_id": {
                "type": "string"
              },
              "kind": {
                "type": "string"
              },
              "next_action": {
                "type": [
                  "object",
                  "null"
                ]
              },
              "result": {},
              "retry_after_seconds": {
                "type": "integer"
              },
              "status": {
                "type": "string"
              },
              "structured_result": {}
            },
            "required": [
              "job_id",
              "status"
            ],
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": true,
            "openWorldHint": false,
            "readOnlyHint": true
          }
        },
        {
          "name": "build_app",
          "title": "Build and deploy a web app from a description",
          "description": "Turn one plain-language description into a LIVE single-page web tool: code is generated, deployed to managed hosting with HTTPS, and listed — you get the public URL in ~1-2 minutes. Best for tool-style apps: calculators, converters, checklists, timers, generators, small games. Async — poll with check_job. Example — tools/call build_app {\"description\":\"a tip calculator web app\"} → poll check_job",
          "inputSchema": {
            "properties": {
              "description": {
                "description": "What the tool should do, in any language. Be specific about inputs/outputs.",
                "type": "string"
              },
              "name": {
                "description": "Optional short app name (defaults to the description).",
                "type": "string"
              },
              "refine": {
                "description": "Slug of an app you built earlier (e.g. \"u-1a23e679\") to modify instead of building from scratch — describe only the change in `description`.",
                "type": "string"
              },
              "visibility": {
                "description": "\"public\" (default, listed in the store) or \"unlisted\" (URL-only, not in the store).",
                "type": "string"
              }
            },
            "required": [
              "description"
            ],
            "type": "object"
          },
          "outputSchema": {
            "additionalProperties": true,
            "type": "object"
          },
          "annotations": {
            "destructiveHint": false,
            "idempotentHint": false,
            "openWorldHint": false,
            "readOnlyHint": false
          }
        }
      ]
    },
    "name": "ai-netcafe",
    "note": "Indexed from the official MCP registry: facts and our own checks, not reviewed, so no score, grade or rank.",
    "packages": [
      {
        "registryType": "npm",
        "identifier": "ai-netcafe",
        "version": "1.2.1",
        "transport": "streamable-http"
      },
      {
        "registryType": "pypi",
        "identifier": "ai-netcafe",
        "version": "1.2.2",
        "transport": "streamable-http"
      }
    ],
    "pageJsonUrl": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe.json",
    "popularity": {
      "githubStars": 1,
      "npmWeekly": 47,
      "pypiWeekly": 16
    },
    "registryName": "com.ainetcafe/ai-netcafe",
    "remotes": [
      {
        "type": "streamable-http",
        "url": "https://ainetcafe.com/mcp?s=registry"
      }
    ],
    "repository": "https://github.com/mario03690/ai-netcafe",
    "reviewed": false,
    "slug": "ainetcafe-ai-netcafe",
    "source": "the official MCP registry",
    "sourceUrl": "https://registry.modelcontextprotocol.io/v0.1/servers?search=com.ainetcafe/ai-netcafe",
    "summary": "Tables and ledgers checked by arithmetic, not by a model. 24 tools. MCP 2026-07-28 ready.",
    "updatedAt": "2026-08-14T13:30:22Z",
    "url": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe",
    "vendor": "ainetcafe.com",
    "vendorUrl": "https://ainetcafe.com/mcp.html",
    "version": "1.7.0",
    "websiteUrl": "https://ainetcafe.com/mcp.html",
    "where": "both",
    "why": [
      "vendor"
    ]
  }
}