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Glama

Gov Bids

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

A4.4/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations, including graceful degradation of partial failures, timing for bundlephobia first measurement (5-30s), and that sources_failed lists timeouts. 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 front-loaded with purpose and provides essential details, but is somewhat lengthy. Every sentence earns its place, though minor trimming could improve conciseness without losing information.

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, the description thoroughly explains return values (summary block, advisories, links, alternative versions) and notes partial failures and timing. It is complete for an agent to understand what the tool returns and its behavior.

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 description coverage is 100% for both parameters, so the description does not need to add much. The description provides context but does not add significant semantic meaning beyond what the schema already documents.

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 performs a composite check for npm packages combining deps.dev and bundlephobia data. It specifies the ecosystem (NPM only) and distinguishes from sibling tools by noting other ecosystems fall under a different tool.

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?

The description explicitly says 'Use whenever an agent asks is X safe / popular / small' and provides use case examples. It also mentions partial failures and timing caveats for bundlephobia, but does not explicitly state when not to use the tool.

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
Disambiguation4/5

Most tools have clearly distinct purposes, but the multiple Pipeworx query variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and multiple Polymarket tools could cause some confusion. However, descriptions are detailed enough to differentiate them.

Naming Consistency3/5

Naming patterns are mixed: some tools use verb_noun (list_subscriptions, open_bids_search), others use noun_phrase (entity_profile, bet_research), and cases are inconsistent (snake_case vs underscores). While not chaotic, the lack of a strong consistent pattern reduces coherence.

Tool Count2/5

33 tools is high, and the server's name 'Gov Bids' suggests a focused scope, but most tools are unrelated (AI visibility, npm packages, general data queries). The tool count feels excessive for a focused server, and the scope is too broad.

Completeness4/5

For a general-purpose data server, the tool set is quite comprehensive across multiple domains (SEC, FDA, economics, prediction markets, etc.). Minor gaps exist (e.g., government contracts beyond bids), but overall coverage is strong.

Resources