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Glama

Wiktionary

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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Adds significant behavioral context beyond annotations: fans out across two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30 seconds, and a `sources_failed` list is returned. No contradiction with annotations.

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

Conciseness4/5

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

The description is a single dense paragraph but packs all necessary information without fluff. It is front-loaded with the core purpose. While efficient, could benefit from bullet points for easier scanning.

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 the tool's complexity, no output schema, and multiple sources, the description comprehensively covers inputs, behavioral details, output structure (summary block, per-advisory detail, links, alternative versions), and limitations (NPM only, timing caveats).

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

Parameters4/5

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds extra value by giving an example of scoped packages (@types/node) and clarifying that version defaults to latest, which aids agent understanding.

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 specifies that the tool performs a composite check for npm packages, combining data from deps.dev and bundlephobia. It uses specific verbs like 'scan' and 'fans out', and distinguishes itself from sibling tools by focusing on the npm ecosystem and providing a combined view.

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?

Explicitly states when to use: when an agent asks about safety, popularity, size, or cost of adding an npm package. Also provides exclusion guidance: other ecosystems like PyPI, Maven, Cargo, Go fall under a different tool (deps.dev:version directly).

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

B3.2/5.0
Disambiguation2/5

The five Wiktionary tools are distinct, but the set is dominated by Pipeworx/prediction-market/memory tools with heavy overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded/deep_research/discover_tools all cover routed lookup. An agent would struggle to pick between these overlapping research entry points.

Naming Consistency2/5

Most names use snake_case, but the style is inconsistent: noun-only names (definition, summary, etymology) sit alongside verb_phrase names (validate_claim, scan_dependency, ask_pipeworx) and compound names (recent_changes, polymarket_edges). No consistent verb_noun or resource_action convention is applied across the set.

Tool Count1/5

36 tools is far too many for a Wiktionary server, and only 5 of them (definition, etymology, pronunciations, search, summary) actually serve Wiktionary. The remaining 31 are unrelated utilities (Pipeworx data routing, Polymarket betting, memory, subscriptions), making the count an extreme mismatch with the server's stated purpose.

Completeness3/5

For basic Wiktionary lookups the surface is usable: search, summary, parsed definitions, etymology, and pronunciations cover the core read path. However there are notable gaps for a dictionary server—no full-entry/wikitext fetch, translations, synonyms, usage examples, or random/word-of-the-day access—and the presence of dozens of unrelated tools does nothing to fill those gaps.

Resources