Lara Translate API by Translated

# Fix list: Lara Translate API

From Anchor Terminal's listing at https://www.anchorterminal.com/tools/lara-translate, the October 2026 research run, assessed 1 October 2026. Grade C, 61.7 out of 100.

This is everything the published grade says the listing lacks, the biggest possible gain to the total first. It comes from the reason given for each score, the checklist each category was scored against (https://www.anchorterminal.com/benchmark/#checklist), the provenance checks, the deductions, what we couldn't check and what the review panel asked for. A fix counts at the next check, once it's public.

For a coding agent working on Lara Translate API: work through the items below in the product, its docs and its public pages. Each category gives the reason for its score, with the points each checklist item earned, and the checklist itself, so the gap is the items that earned less than their points. Change the product, not the wording, and keep a note of what you changed and where it's published.

## 1. Security & auth, 30 out of 100, up to 12.3 more on the total

Why it scored 30: Model reading of the checklist, with training and retention in place of the least-privilege and injection lines. An access key ID and secret that the SDKs use to sign each request, and OAuth with a Lara account on the hosted MCP. No key scopes found (20). The developer docs say data may be used for model improvement by default, and Incognito is opted into per request with `noTrace` (5 of 20). Learning Mode stores translation and context texts with no stated retention period. Incognito keeps text in memory only, and account content is deleted within 90 days of cancellation (5 of 15). No audit log or per-call usage view found (0). laratranslate.com has no security.txt per the 30 September check, and we found no disclosure policy, bug bounty or certification on the pages we read (0).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-security):

- 0 to 30, the credential model. 30 for OAuth 2.1 with scopes, or scoped and revocable keys with rotation. 20 for plain revocable API keys. 10 for one all-powerful key. 10 off when a secret can travel in a URL query string as a documented option.
- 0 to 20, read-only or least-privilege modes, and confirmation or approval for destructive actions.
- 0 to 15, prompt-injection posture where the tool returns untrusted content (documented mitigations or guidance). A tool that returns no untrusted content gets 10.
- 0 to 15, audit logs or per-call visibility for the operator.
- 0 to 20, a security programme. security.txt or a disclosure policy, a bug bounty, SOC 2 or ISO 27001, advisories handled in public.

Models are read for retention, whether API data trains models (and whether that's off by default), zero-retention options and certifications. Frameworks for telemetry defaults, approval hooks, guardrails and sandboxing.

## 2. Payments & pricing, 40 out of 100, up to 7.5 more on the total

Why it scored 40: No x402, MPP or L402 (0). Per-million-character prices for Standard, Think and Prosa published in dollars and euros without a login (20). The Free plan includes 10,000 API characters a month with no credit card required (20). A person signs up in a browser, and the hosted MCP signs in with a Lara account (0).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-payments):

The published rubric, also on the [x402 page](https://www.anchorterminal.com/x402/).

- 40, a machine payment protocol (x402, MPP or L402) on the tool's own endpoints. 10 to 30 when it covers only some endpoints or only goes through a third party, and the note says which.
- 20, per-call or per-unit pricing published without a login. 10 for public plan-only pricing, 0 for "contact sales" or prices behind a login.
- 20, a free tier or trial that doesn't need a card.
- 20, autonomous onboarding, meaning an agent can get access without a person signing up in a browser (keyless use, x402, a programmatic key API).

Payment platforms and agent wallets rarely charge for their own API over a machine protocol, so the first line has steps for them, and the highest one that applies counts. 40 when x402, MPP or L402 runs on all their own endpoints, 30 when it runs on part of their own API, 25 when their merchants can accept one, 20 for running a facilitator, 15 for paying as a buyer, and 0 when the only protocol is their own. Merchant acceptance sits above a facilitator because the platform's own customers can charge agents through it, while a facilitator settles for sellers who wire up the protocol themselves. The counter-argument (a facilitator does more for the protocol as a whole) has a point. Each note says which step applied.

Open-source software you run yourself is scored on its hosted or paid option if it has one. A free, self-hosted package with nothing to buy gets 20, 20 and 20 for the last three lines, and 0 to 40 for the first only if it ships a payment protocol.

## 3. Reliability, 70 out of 100, up to 6 more on the total

Why it scored 70: Atlassian Statuspage at status.laratranslate.com with Lara Translation API as its own component (20). The front page read All Systems Operational with no incidents in the window our reader saw, but the history page didn't render its list and robots.txt blocks the JSON feed, so we score it as minor-only rather than clean (20). Rate limits published, 1,000 characters a second and 10,000 a month on the free tier, 10,000 characters a second on paid plans (15). No documented status code for a breached limit and no retry or backoff advice (0). The terms state a guaranteed uptime of 98 per cent or higher, with refunds at Translated's discretion, which is weaker than a credit schedule (5 of 10). The SDKs and hosted MCP are GA. The raw REST API is on request only (10).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-reliability):

Hosted APIs, MCP servers, models and platforms.

- 20, a public status page with component history (Statuspage, Instatus, BetterStack or the vendor's own).
- 0 to 30, the incident record for the last 90 days on that page. 30 for a clean record or trivial incidents only, 20 for minor incidents only, 10 for one major outage (an hour or more of a core API down, or errors across the board), 0 for several. 5 when there's no history we could read, and the note says so.
- 15, rate limits documented with numbers.
- 15, documented 429 or overload handling (Retry-After, backoff guidance), and idempotency keys or safe-retry guidance where writes are involved.
- 10, an SLA published for any paid tier.
- 10, the surface agents use is generally available, not beta or preview.

Local packages, SDKs, frameworks and stdio MCP servers.

- 20, installs from an official package with supported runtimes stated.
- 25, a public CI and test suite, passing on the default branch.
- 0 to 25, open crash or regression issues relative to activity (25 for few and handled, 0 for many, old and unanswered).
- 15, semver discipline and breaking changes called out in a changelog.
- 15, version 1.0 or later, or declared stable.

Protocols are read from their reference implementations, the public facilitators or servers, spec stability and test vectors.

## 4. Schema & documentation, 74 out of 100, up to 4.2 more on the total

Why it scored 74: No public OpenAPI. The official MCP server publishes typed JSON Schema inputs and output schemas on all 22 tools, which is the contract an agent sees (20 of 25). llms.txt with about 30 entries (10). Tool descriptions say to call `list_glossaries` or `list_memories` first to resolve names, to send one target language per call, and to add instructions only when needed (18 of 20). Inputs typed with ID formats (`gls_*`, `mem_*`), a maximum of 10 glossaries and 20-word instructions, but language codes are free strings (13 of 15). SDK examples, and the SDKs raise typed errors for authentication, quota and validation, but no error code list was found (8 of 15). No public API changelog. The MCP repository and package versions are the only dated record (5 of 15).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-schema):

APIs and MCP servers.

- 25, a machine-readable contract (a public OpenAPI file or similar; for MCP, typed JSON Schema inputs on every tool).
- 10, llms.txt or Markdown docs served for agents.
- 0 to 20, descriptions that say what a tool is for, when to use it and when not to, read from the tool definitions in the source or the API reference.
- 0 to 15, typed inputs with enums, constraints and required fields, and no free-form JSON blobs.
- 0 to 15, examples and documented error responses.
- 15, versioning and a public changelog.

Models are read from the API reference, the OpenAPI file, llms.txt, the structured-output and tool-use docs and the model cards. Frameworks from docs a model can follow, typed interfaces, examples and the API reference.

## 5. Agent ergonomics, 77 out of 100, up to 3.7 more on the total

Why it scored 77: MCP reading of the checklist. 22 tools, three for translation, detection and the language list, 8 for translation memories and 11 for glossaries, with no read-only subset or toolsets (15). Translate takes an array of text blocks with a translatable flag per block, but list tools show no paging controls (12 of 20). Since 2.0.0 tool failures come back as `isError` results with messages instead of protocol errors (15 of 20). Every tool sets readOnlyHint, destructiveHint and idempotentHint, with deletes, updates and imports marked destructive (20). Translate needs only text and a target, and SDKs exist for Python, Node.js, Java, PHP and Go (15).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-ergonomics):

- 0 to 25, context cost. For MCP, the number and size of the tool definitions (25 for ten or fewer compact tools, 15 for 11 to 30, 5 for more than 30, plus up to 10 back for toolsets, dynamic loading or read-only subsets). For APIs, whether responses can be sized (field selection, limits, summaries).
- 20, pagination, filtering and output-size controls.
- 20, actionable, documented error responses, codes and messages an agent can recover from.
- 20, idempotency or safe retries, and for MCP the `readOnlyHint` and `destructiveHint` annotations.
- 15, sensible defaults, few required parameters, and official SDKs in at least two languages.

Models are read for tool use, structured output, prompt caching, context length, batch and SDKs. Frameworks for how much code and how many defaults a tool-calling agent with MCP needs.

## 6. Transparency & trust, 64 out of 100, up to 3.2 more on the total

Made of editorial 52, provenance 76.

Why it scored 64: Closed service under published terms, SDKs and MCP server under MIT (15). The terms describe Learning Mode storing texts to improve Lara's model, the developer docs say that's the default, and the privacy policy says some data isn't used for AI model training without making clear which. No retention period for Learning Mode texts (12 of 30). ModernMT's sunset is dated (keys work until 2026-12-31) with an endpoint map, and the terms promise 30 days' notice of material changes (15 of 20). The DPA points to a sub-processor and data location list we didn't open (10 of 20).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-transparency):

- 0 to 30, source availability and licence clarity. 30 for open source under an OSI licence, 15 for closed with clear terms, 0 for unclear terms.
- 0 to 30, data handling and retention statements that agree with each other (privacy policy, DPA, retention periods, subprocessors).
- 0 to 20, a deprecation policy or notices with dates.
- 0 to 20, telemetry disclosed with an opt-out (local software), or subprocessors and data locations disclosed (hosted).

The other half of Transparency and trust is the provenance score, computed from checked facts (below). The category score is the mean of the two.

Provenance checks not met in full (half of this category, computed from checked facts):

- Domain age: laratranslate.com, registered 2017-06-04 (9 years) (11 of 15)
- Changelog: not found (0 of 10)
- security.txt: not found (0 of 10)

## 7. Maintenance & community, 83 out of 100, up to 1.5 more on the total

Why it scored 83: `@translated/lara-mcp` 2.0.0 and `@translated/lara` 1.16.0 both shipped on 2026-09-24 (30). Python SDK 1.11.3 and 1.12.0 in July, plus the two September releases (20). No public API changelog and we didn't read the GitHub issues. Support runs through support@laratranslate.com and a knowledge base (8 of 15). Official SDKs current in five languages, the Python one supports 3.8 and later (15). The MCP server runs tests on every push and pull request, and 2.0.0 cleared all open dependency advisories per its release note (10).

The checklist (https://www.anchorterminal.com/benchmark/#checklist-maintenance):

- 0 to 30, time since the last release, or the last published model or API change for a closed service. 30 within 30 days, 20 within 90, 10 within 180, 0 older.
- 20, at least three releases or dated changelog entries in the last 90 days.
- 0 to 25, responsiveness. Issues and pull requests answered on GitHub (the open issues and how recent the replies are). For closed services, a public changelog and a support or community channel that answers, 0 to 15.
- 15, presence in the official MCP registry under a verified namespace (MCP servers), or current official SDKs (APIs and models).
- 10, package health, current dependencies and CI.

Models are read for deprecation notice periods and model churn rather than release counts.

## What we couldn't check

What we couldn't read counted as absent. Publishing it on a page a plain HTTP fetch can read (not only in a browser) lets the next check count it.

- The listing said no status page. We found an Atlassian Statuspage at status.laratranslate.com and the patch adds it to provenance.
- unchecked: the full incident list on status.laratranslate.com, since the history page didn't render for our reader and robots.txt blocks the JSON feed.
- The privacy policy says some data isn't used for AI model training while the terms and docs describe Learning Mode storing texts to improve the model. We couldn't tell which data the privacy sentence covers.
- The Node SDK date (2026-09-24) comes from npm registry metadata, since the lara-javascript repository needed credentials to clone.

## Weaknesses

- Texts may be used for model improvement unless a request sets `noTrace`
- No public REST API or API changelog, so every call goes through an SDK or the MCP server
- No security.txt, disclosure policy or certification found
- Lara Think costs $2,499 per million characters on Pro
- No documented error code or retry advice for rate limits

## What costs an agent a turn today

The notes we give agents before they call it. Each one is a workaround an agent shouldn't need.

- Set `noTrace` on every request that carries private text, since storage for model improvement is the default
- Leave `reasoning` off unless the text needs it. It moves the call to Lara Think at $2,499 per million characters
- Resolve glossary and memory names to `gls_*` and `mem_*` IDs with the list tools before translating
- Send one target language per translate call
- Merge short documents, since each file bills at least 20,000 characters

## What the review panel asked for

- Cap spend per key
- Document the reasoning price
- publish an OpenAPI spec
- storage-free default

## When it's done

Send what changed and where it's published as a dispute (https://www.anchorterminal.com/builders/#disputes, or `POST https://www.anchorterminal.com/api/v1/contact` with `"kind": "dispute"`). Disputes are answered in public, and the listing is checked again by the same checklist. Paying for an audit or a listing claim changes nothing here.