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Glama

Apis Guru

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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description adds valuable behavioral details: partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists timeouts. No contradictions 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 fairly concise given the complexity of the tool. It is front-loaded with the purpose and key features. While it contains a lot of detail, every sentence contributes meaning. Slightly verbose but appropriately so.

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 that there is no output schema, the description fully explains the return structure: summary block, per-advisory details, links, and alternative versions. It also covers constraints (npm only v1) and partial failure behavior. Complete for a complex 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?

The input schema already provides descriptions for both parameters with 100% coverage. The description adds value by specifying that scoped packages (e.g., '@types/node') are accepted and that version defaults to latest. This adds nuance 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 the tool's purpose: performing a composite check for adding an npm package, covering license, advisories, bundle size, dependency count, and ESM/tree-shake support. It distinguishes itself from siblings by explicitly limiting to the npm ecosystem.

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?

The description provides explicit usage context: questions like 'is X safe / popular / small' or 'what does adding lodash cost me'. It also specifies when not to use (non-npm ecosystems) and points to alternative (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

A4/5.0
Disambiguation3/5

Several tools have overlapping purposes—ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-twins (beta currently matches the stable router exactly), and deep_research, entity_profile, recent_changes, and compare_entities all fan out across similar data sources. The long descriptions do help differentiate them, but an agent selecting quickly could easily pick the wrong variant.

Naming Consistency3/5

Most tools use snake_case, but the verb style is inconsistent: get_api, list_providers, and validate_claim use verb_noun, while remember/forget/recall are bare verbs and polymarket_arbitrage, entity_profile, and bet_research are noun phrases. The pattern is readable but not predictable enough to infer behavior from the name alone.

Tool Count2/5

At 35 tools, the surface is heavy, and many are hyper-specialized (five separate Polymarket tools, three ask_pipeworx variants, three memory tools). The breadth is defensible for a multi-domain data platform, but it goes past the comfortable 16-25 range and would benefit from consolidation.

Completeness4/5

The tool set covers the main research lifecycle well: discovery, routing, grounded answers, entity resolution, profiling, comparison, claim validation, change tracking, subscription management, and memory. Minor gaps exist—such as no explicit fetch-by-citation-URI tool and soft-failed patent coverage—but agents can generally work around them.

Resources