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Glama

Usaspending

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
Behavior5/5

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

Annotations already declare readOnly, idempotent, openWorld, not destructive. Description adds rich behavioral details: fans out across deps.dev and bundlephobia, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed lists failures. 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?

Description is information-dense and front-loaded with main purpose, then details. Every sentence adds value, though slightly verbose. Well-structured for the 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?

Given no output schema, description comprehensively covers return value shape (summary block fields, per-advisory detail, links, alternative versions) and failure modes. With clear schema and annotations, the description is fully complete.

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 descriptions for both parameters. Description adds minor context (scoped packages accepted, version defaults to latest) but mostly reinforces schema. No additional syntactic or format details beyond 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?

Description clearly states the tool does a composite check for npm packages covering license, advisories, version history, bundle size, dependency count, and ESM/tree-shake support. It distinguishes from sibling tools by specifying 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly' and provides explicit usage cues like 'is X safe / popular / small'.

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?

Provides explicit when-to-use instructions ('Use whenever an agent asks...') and warns about partial failures and bundlephobia timeouts. Mentions alternatives for other ecosystems. Lacks explicit when-not-to-use scenarios, but the scope is well defined.

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

All Usaspending toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are essentially the same router with different output modes, and ask_pipeworx_beta is currently identical to ask_pipeworx. There is also meaningful overlap between entity_profile, compare_entities, recent_changes, validate_claim, and the USAspending profile/search tools.

Naming Consistency3/5

All names are lower_snake_case with useful prefixes like ask_, polymarket_, and usa_, which helps grouping. However, the underlying convention is mixed: some are verb+object, some are noun phrases, and some are bare verbs, so there is no uniform verb_noun pattern.

Tool Count2/5

38 tools is well beyond the heavy range, and most of them are unrelated to USAspending: Polymarket betting, npm dependency checks, AI visibility, memory storage, and meta-tools. The actual USAspending-specific surface is only about seven tools, making the server feel bloated and unfocused.

Completeness3/5

The federal-contract cluster covers award search, recipient/incumbent profiles, expiring awards, and spending by agency/category/trend, which handles the main contracting questions. Missing award-detail retrieval, grants/assistance coverage, and open-solicitation lookup, which usa_expiring_awards explicitly punts to external samgov/govcon tools.

Resources