{ "data": { "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" ] } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe", "json": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe.md", "slim": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe.min.md" }, "markdown": "# ai-netcafe\n\n\u003e Indexed, not reviewed: facts from the official MCP registry and our own checks. No score, grade or rank, and not in the rankings until the panel reviews it. How the index works: https://www.anchorterminal.com/indexed/\n\n- Kind: MCP server, by ainetcafe.com (https://ainetcafe.com/mcp.html)\n- Category: Accounting \u0026 invoicing (https://www.anchorterminal.com/categories/accounting.md)\n- Listed because: It's published in the registry under ainetcafe.com, a namespace the registry only gives to whoever proves they control that domain.\n- What the official MCP registry says: Tables and ledgers checked by arithmetic, not by a model. 24 tools. MCP 2026-07-28 ready.\n\n## Facts\n\n- MCP registry: `com.ainetcafe/ai-netcafe` 1.7.0\n- Endpoint: https://ainetcafe.com/mcp?s=registry (streamable HTTP)\n- Package: npm `ai-netcafe` (streamable-http)\n- Package: pypi `ai-netcafe` (streamable-http)\n- Source: https://github.com/mario03690/ai-netcafe\n- Website: https://ainetcafe.com/mcp.html\n- npm downloads a week: 47\n- PyPI downloads a week: 16\n- GitHub stars: 1\n- Registry entry updated: 2026-08-14\n\n## Tools\n\n- Tools it lists (34, about 7,810 tokens of context, `tools/list` without credentials, checked 2026-10-04 22:22 UTC):\n - `what_can_you_do` (read-only): 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…\n - `list_apps` (read-only): 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…\n - `get_app` (read-only): 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.…\n - `ask_model` (writes): 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);…\n - `compare_models` (writes): 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…\n - `list_models` (read-only): 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…\n - `remember` (writes): 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…\n - `recall` (read-only): 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…\n - `web_search` (read-only): 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…\n - `fetch_page` (read-only): 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…\n - `model_costs` (read-only): 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…\n - `ai_visibility` (read-only): 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 /…\n - `pdf_to_markdown` (read-only): 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.…\n - `extract_tables` (read-only): 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…\n - `extract_statement` (read-only): 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…\n - `json_yaml` (read-only): 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…\n - `validate_json` (read-only): 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…\n - `diff_text` (read-only): 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…\n - `jwt_decode` (read-only): 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…\n - `regex_test` (read-only): 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…\n - `diff_tables` (read-only): 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…\n - `clean_table` (read-only): 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…\n - `merge_tables` (read-only): 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…\n - `reconcile_ledger` (read-only): 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…\n - `extract_invoices` (read-only): 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…\n - `create_task` (writes): 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…\n - `list_tasks` (read-only): Show scheduled tasks, next run times, run counts, and measured platform cost metadata. User charge is $0.00 during the beta.\n - `get_task_runs` (read-only): Recent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.\n - `delete_task` (writes): Stop and remove a scheduled task and its run history.\n - `transpile_sql` (read-only): Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb,…\n - `china_reachability` (read-only): 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?\"…\n - `render_diagram` (read-only): 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…\n - `check_job` (read-only): 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…\n - `build_app` (writes): 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…\n- How its tools read to an agent (0 errors, 2 warnings, 0 notes, about 7,810 tokens; rules at https://www.anchorterminal.com/check.md; not part of the score):\n - warn TC14 create_task: allowed values are in the description, not an enum: kind\n - warn TC18 merge_tables: readOnlyHint is true but the name says \"merge\"\n\n- JSON: https://www.anchorterminal.com/api/v1/tools/ainetcafe-ai-netcafe.json\n- Being indexed says nothing about quality, and nobody can pay for it. Ask for a review: https://www.anchorterminal.com/builders/#claiming\n", "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" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Terminal", "url": "https://www.anchorterminal.com/tools/" }, { "name": "Indexed", "url": "https://www.anchorterminal.com/indexed/" }, { "name": "ai-netcafe", "url": "" } ], "description": "ai-netcafe, an MCP server by ainetcafe.com, listed from the official MCP registry. Indexed, not reviewed: facts and our own checks, no score or ranking. Tables and ledgers checked by arithmetic, not by a model. 24 tools. MCP 2026-07-28 ready.", "facts": [ "not reviewed", "not ranked", "facts only" ], "h1": "ai-netcafe", "image": "https://www.anchorterminal.com/assets/og/indexed.png", "path": "/tools/ainetcafe-ai-netcafe", "published": "", "section": "indexed", "title": "ai-netcafe: MCP server, indexed from the official MCP registry", "toc": null, "updated": "2026-10-04", "url": "https://www.anchorterminal.com/tools/ainetcafe-ai-netcafe" }, "tokens": { "markdown": 2200, "slim": 2130 }, "version": 1 }