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Glama

Libretranslate

by io.github.pipeworx-io

Scan Dependency

scan_dependency
Read-onlyIdempotent

Composite "should I add this npm package to my project" check in ONE call — fans out across deps.dev (license + advisories + version history) and bundlephobia (gzipped/minified bundle size, dependency count, ESM/tree-shake support). Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me". Returns a summary block (is_latest, license, published_at, advisory_count, bundle_kb_min, bundle_kb_gz, dependency_count, has_esm, tree_shakeable), per-advisory detail, links, and a list of recent alternative versions. NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly. Partial failures degrade gracefully — bundlephobia's first measurement on a new version can take 5-30s; sources_failed will list it if it times out, the rest still returns.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesnpm package name. Scoped packages (e.g. "@types/node") are accepted.
versionNoSpecific version to check (e.g., "18.3.1"). Defaults to the latest published version when omitted.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnly, openWorld, and idempotent annotations, the description discloses important behavioral details: it fans out to multiple services, may experience 5-30s delays on first bundlephobia measurement of a new version, and degrades gracefully with a sources_failed field. These are non-obvious insights that help the agent set expectations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence serves a purpose: the first defines the composite check, the second gives usage triggers, the third lists return fields, and the fourth covers ecosystem limits and failure behavior. It is front-loaded and contains no filler, making it highly efficient for its complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Even though there is no output schema, the description enumerates the summary block fields (is_latest, license, published_at, advisory_count, etc.) and mentions per-advisory details, links, and alternative versions. It also addresses edge cases (NPM-only, timeouts) and explains that partial failures still return useful data, so the agent knows what to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%: both the 'package' and 'version' parameters are already well-described in the input schema. The description adds no further parameter-level detail, so it meets the baseline for high schema coverage but does not exceed it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it is a composite 'should I add this npm package' check that fans out across deps.dev and bundlephobia. It specifies the exact verb (check) and resource (npm package), and the distinctive composite nature distinguishes it from sibling tools like scan_competitor_ai_presence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"', providing clear trigger conditions. It also states an exclusion: non-NPM ecosystems (PyPI, Maven, Cargo, Go) should go directly to deps.dev, not this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to the same underlying Pipeworx catalog, and the five polymarket_* tools all analyze prediction-market opportunities and edge. While the individual descriptions are detailed, an agent could easily select the wrong tool without deep reading, particularly between ask_pipeworx and its beta/variant versions.

Naming Consistency2/5

The naming style is a mixture of imperative verb phrases (translate, validate_claim, forget, generate_llms_txt), noun phrases (entity_profile, recent_alerts, polymarket_arbitrage), and brand-prefixed nouns (ask_pipeworx, pipeworx_trending, bet_research). While all names are lowercase with underscores, there is no consistent verb_noun or domain-prefix convention across the toolset, making the API feel grab-bag rather than designed.

Tool Count1/5

The server is named 'Libretranslate' — a translation service that needs only translate, detect_language, and list_languages — yet it exposes 34 tools spanning data research, prediction markets, memory storage, subscriptions, dependency scanning, AI-visibility probing, and llms.txt generation. This is an extreme scope mismatch: the overwhelming majority of tools serve completely unrelated functions that have nothing to do with the server's apparent purpose.

Completeness2/5

If judged purely as a translation server, the core surface is present but thin: translate, detect_language, and list_languages cover basic use, though there are no batch, format, or language-details options. If judged as the broader heterogeneous toolset, the domain is incoherent — no single workstream is fully covered, and the unrelated tools (Polymarket betting, Pipeworx research, memory, subscriptions) create a muddled surface with obvious gaps in any single stated purpose.

Resources