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Glama

Data Issy

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description enriches this by explaining the fan-out to external sources, partial failures (bundlephobia timeout), and that sources_failed will be listed. 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 slightly long but every sentence serves a purpose. It is well-structured with the main purpose first, then details. Minor reduction possible but efficient overall.

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, but the description thoroughly explains the return value (summary block, details, links) and covers input semantics, defaults, and edge cases (partial failures, ecosystem scope). Complete for a data-gathering 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 coverage is 100% so baseline is 3. The description adds context: default version is latest, scoped packages are accepted, which adds value beyond the schema.

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 check for npm packages covering license, advisories, bundle size, etc. It distinguishes from siblings by specifying the npm ecosystem and noting alternatives for other ecosystems.

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 tells when to use (when an agent asks about safety, popularity, size) and provides exclusions (PyPI/Maven/Cargo/Go) with a pointer to deps.dev:version.

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

A3.9/5.0
Disambiguation3/5

The toolset is mostly organized by clear subdomains, but there are multiple overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer research questions and the beta version is currently identical to the stable router. Detailed descriptions reduce confusion, but an agent could still reasonably pick the wrong one for a given task. The entity, memory, and subscription tools are more clearly separated.

Naming Consistency3/5

Names are consistently lower_snake_case and readable, but the set mixes verb-led names (compare_entities, resolve_entity, validate_claim) with noun-led names (entity_profile, polymarket_edges, pipeworx_trending) and some odd pairings like ai_visibility_check vs scan_competitor_ai_presence. No chaotic camelCase or inconsistent separators, but the convention is not uniform enough for a strong score.

Tool Count2/5

34 tools is past the 25+ threshold and the surface spans many unrelated domains: structured data lookup, prediction markets, AI visibility marketing, city open data, npm dependency checking, llms.txt generation, memory, and subscriptions. Each tool may be individually useful, but the collection feels like a platform dump rather than a tightly scoped server. A more focused server would split off prediction markets, AI visibility, and utility tools.

Completeness4/5

The main data-research workflow is well covered: discovery, routing, grounded answering, deep research, entity resolution, profiles, comparisons, recent changes, claim validation, and search-within-results are all present. Prediction-market analysis, memory, and subscription lifecycles also have no major dead ends. Minor gaps exist, such as no write/update path for open data and no subscription option for AI-visibility monitoring, but these are not central to the apparent core purpose.

Resources