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Glama

Tatoeba

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?

Description goes well beyond annotations by disclosing partial failure degradation, bundlephobia's 5-30s first-measurement latency, and the sources_failed field. This gives agents realistic expectations of behavior without contradicting the readOnly/idempotent annotations.

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?

Though long, the description is densely informative and well-structured: purpose, usage, return summary, ecosystem caveat, failure behavior. Every clause contributes meaning; no filler or redundancy.

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?

Given a complex composite tool with no output schema, the description fully enumerates return values (summary block fields, per-advisory detail, links, alternatives), constraints (NPM-only), and edge cases (timeout behavior). It is self-sufficient for an agent to predict results.

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 coverage is 100% and both parameters (package, version) are fully described there. The description reinforces defaults ('latest') and scoped package acceptance, but adds no new semantic detail beyond schema, matching the baseline for high coverage.

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?

Purpose is exceptionally clear: 'Composite "should I add this npm package to my project" check in ONE call' immediately identifies the tool's function. It specifies the verb (scan), resource (dependency), and distinct data sources (deps.dev, bundlephobia), distinguishing it from all sibling tools.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also notes NPM-only scope and directs users to deps.dev:version for other ecosystems, effectively naming an alternative.

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

B3/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research; multiple Polymarket tools). The memory tools (remember/recall/forget) are generic and could be confused with each other. Overall, there is significant ambiguity in tool selection.

Naming Consistency3/5

Most tool names use snake_case, but there is no strong verb_noun pattern. Some are simple nouns (languages, search) while others are verbs (forget, recall). The naming is readable but not highly consistent.

Tool Count1/5

With 34 tools, the set is far too large for a Tatoeba server. Only 4 tools (search, sentence, translations, languages) are actually related to Tatoeba; the rest cover unrelated domains (Pipeworx data, Polymarket, AI visibility). This is an extreme mismatch.

Completeness2/5

For the Tatoeba domain, the 4 tools cover basic functionality but lack operations like adding or editing sentences. The overwhelming presence of irrelevant tools makes the surface feel incomplete and disjointed for the server's stated purpose.

Resources