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Glama

Fmcsa

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

A4.6/5.0
Behavior5/5

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

Annotations already mark it as readOnly and idempotent. Description adds behavioral details: it fans out across two services, partial failures degrade gracefully, bundlephobia's first measurement can take 5-30s, and sources_failed will list timeouts. This is excellent transparency.

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 (about 150 words) but well-structured: composite nature, use cases, return value, ecosystem limitation, failure behavior. Every sentence adds value, and the core purpose is front-loaded.

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 the complexity and the lack of output schema, the description provides a detailed summary of what is returned (summary block, advisory details, links, alternative versions) and explains failure handling. It is complete for the tool's purpose.

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%, so baseline is 3. The description does not add much beyond the schema for parameters; it mentions that version defaults to latest when omitted, which is already in the schema. However, the explanation of the return value provides context for parameter usage.

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, aggregating license, advisories, version history, bundle size, dependency count, and ESM/tree-shake support from deps.dev and bundlephobia. It is distinct from sibling tools, none of which mention npm or package analysis.

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 agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. Also provides exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly', guiding towards alternatives for other ecosystems.

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

All Fmcsa toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is notable overlap: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical, as are bet_research and polymarket_edges. Detailed descriptions help, but an agent could still struggle to pick the right one.

Naming Consistency2/5

Naming conventions are mixed: snake_case (fmcsa_carrier_lookup), verb_noun (ask_pipeworx, recall), and phrases (suggest_questions, generate_llms_txt). No consistent pattern, making it harder for an agent to infer tool purpose from name alone.

Tool Count3/5

35 tools is high for a single server, but the server aggregates many domains. While each tool may serve a purpose, the count feels bloated and beyond typical scope (3-15). Some tools could be merged (e.g., the ask_pipeworx variants).

Completeness2/5

For the FMCSA domain, the four tools provide decent coverage. However, the server includes many tools for other domains (e.g., prediction markets, company profiles) without full lifecycle support (e.g., only lookup, no create/update). The set feels like a random collection rather than a coherent domain.

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