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Glama

Samgov

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint. Description adds detailed behavior: partial failure degradation (sources_failed), retry semantics (first measurement timeout), and exact return structure (summary block with fields, advisories, links, alternatives). 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?

Description is long but front-loaded with the core purpose, then branching into detail. Every sentence provides essential context (ecosystem, failures, output structure). Minor redundancy ('NPM ecosystem only in v1' appears twice), but overall well-organized and efficient.

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?

Despite no output schema, description enumerates 8 summary fields, per-advisory details, links, and alternative versions. Covers failure modes, timeout behavior, and ecosystem scope. Comprehensively explains the tool's return value for agents to interpret results.

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% with clear field descriptions. Description adds value by noting scoped packages (@types/node) accepted and that version defaults to latest. Also explains that the tool aggregates data from multiple sources, helping understand the parameter roles in a composite context.

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 verbs ('scan', 'composite check') and clearly identifies the resource (npm package) and the multi-source fan-out (deps.dev, bundlephobia). Distinguishes from sibling tools by explicitly stating the ecosystem scope (NPM only) and the single-call nature.

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 ('is X safe / popular / small', 'what does adding lodash cost me'), ecosystem limitations (NPM v1; other ecosystems via deps.dev:version directly), and timeout behavior (bundlephobia first measurement 5-30s). Provides clear alternatives for non-NPM packages.

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

A3.8/5.0
Disambiguation2/5

Three ask_pipeworx variants and a dense cluster of polymarket_* edge tools have heavily overlapping purposes, and ai_visibility_check vs scan_competitor_ai_presence further blurs boundaries. Only the sam_*, memory, and subscription tools form cleanly distinct families.

Naming Consistency3/5

All names are lowercase snake_case and readable, but the pattern is mixed: verb_noun names (compare_entities, resolve_entity), bare verbs (remember, recall, forget), noun phrases (entity_profile, polymarket_edges), and domain-prefix families (sam_*, polymarket_*) coexist. No camelCase chaos, but no consistent verb style either.

Tool Count2/5

36 tools is well beyond the ideal range, and many are near-duplicates or wrappers (ask_pipeworx variants, ai_visibility_check vs scan_competitor_ai_presence). The server is named Samgov, yet only 5 tools actually concern SAM.gov, making the count feel inflated and unfocused.

Completeness3/5

The SAM.gov subset covers entity search, opportunities, set-asides, opportunity details, and exclusions, but omits major datasets like contract awards. The broader Pipeworx research/memory/subscription surface is extensive, though it is muddled by redundant query modes and lacks a direct way to invoke individual pack tools.

Resources