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Glama

What3words

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

A4.6/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, idempotentHint, etc. Description adds valuable behavioral details: external calls to deps.dev and bundlephobia, potential 5-30s delay on new versions, partial failure handling (sources_failed). 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?

Single paragraph but well-structured: purpose first, then usage, return details, limitations, edge cases. No redundancy, each sentence adds value. Slightly long but justified by tool 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?

No output schema, so description fully covers return structure: summary block, per-advisory detail, links, alternative versions. Also addresses error handling (sources_failed) and ecosystem scope. Comprehensive for a composite tool.

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 has 100% coverage with descriptions for both parameters. Description adds extra meaning: scoped packages example and default behavior for version. Also explains return fields, indirectly aiding parameter 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?

Description uses specific verb 'scan' and resource 'npm package' with clear purpose: determining if a package should be added. Differentiates from sibling tools (e.g., resolve_entity) by focusing on npm ecosystem and composite check.

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

Usage Guidelines4/5

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

Explicitly states when to use: when agent asks about safety/popularity/size/cost. Mentions limitations: NPM only in v1, suggests other ecosystems fall under deps.dev:version directly (though not naming specific tool). Lacks explicit alternative tool names.

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

All What3words toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

There is significant overlap among the meta-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions, and validate_claim all route to the same underlying data catalog with only subtle differences in grounding or scope. Company-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also share fuzzy boundaries. The four what3words tools are clearly distinct, but they sit awkwardly beside a much larger, partially redundant Pipeworx/prediction-market cluster.

Naming Consistency3/5

All tool names use snake_case, which provides a base level of consistency, but the naming patterns vary widely: some are verb_noun (list_languages, recall, forget), some are X_to_Y (coords_to_words, words_to_coords), some are brand-prefixed (pipeworx_*, polymarket_*), and some are bare concepts (deep_research, entity_profile, autosuggest). The ask_pipeworx family is internally consistent, as are the polymarket_* tools, but the overall set lacks a single predictable convention.

Tool Count2/5

35 tools is well above the 25-tool threshold, and the server is named What3words when only 4 of the 35 tools actually belong to that geocoding domain. Even interpreted as a general data platform, 35 tools with a heavily overlapping meta-tool layer feels bloated rather than well-scoped. The what3words-specific surface would be appropriately sized at 4-5 tools on its own.

Completeness3/5

For what3words specifically, the surface is complete: coords_to_words, words_to_coords, autosuggest, and list_languages cover the core bidirectional conversion plus discovery. However, for the broader domain the server actually serves, there are notable gaps such as no direct resolve-by-pipeworx://-URI tool and no open-web search, despite citations and external data being advertised as fetchable. The mix of geocoding, data lookup, prediction markets, memory, and subscriptions makes it unclear what complete coverage would even mean.

Resources