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Glama

Wordnik

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

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds meaningful behavioral detail: partial failures when bundlephobia times out (5-30s) and graceful degradation via sources_failed field, which annotations don't cover.

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 front-loaded with purpose and usage, followed by details. It's slightly verbose with parenthetical explanations but structured well for readability. Could tighten without losing value.

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 no output schema, the description fully explains the return structure (summary block, advisories, links, alternatives) and handles failure modes. Everything an AI needs to use the tool correctly is covered.

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% with clear descriptions for package and version. The description adds no additional semantics beyond that baseline, so a score of 3 is appropriate.

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 defines the tool as a composite check for 'should I add this npm package?' covering license, advisories, bundle size, etc. It distinguishes itself from siblings by specifying the NPM ecosystem and the services it fans out to.

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: 'whenever an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. Also notes ecosystem limitation and provides alternative for other package managers via deps.dev 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

B3.1/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially between Wordnik and Pipeworx domains. However, some overlap exists among data query tools (e.g., ask_pipeworx vs deep_research) and company lookups (entity_profile vs compare_entities), but descriptions are detailed enough to differentiate them in most cases.

Naming Consistency3/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others use descriptive phrases (ask_pipeworx_grounded), and some are single words (remember, recall). There is no uniform verb_noun pattern, though groups like polymarket_* and scan_* provide some consistency within their subsets.

Tool Count2/5

With 42 tools, the server is overloaded. It combines two distinct services (Wordnik dictionary and Pipeworx data) into one set, making it feel like two servers merged. Many tools are niche (e.g., hyphenation, random_words), increasing count without clear benefit. A split would improve coherence.

Completeness4/5

The Wordnik coverage is thorough (definitions, examples, pronunciation, frequency, etc.), and Pipeworx covers a wide range of data sources with tools for basic lookups, comparisons, research, and subscriptions. Minor gaps exist (e.g., no update/delete for Wordnik data), but overall the surface is comprehensive for the intended use.

Resources