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Glama

Fcc Ecfs

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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses important behaviors: composite fan-out across multiple sources, potential timeout on bundlephobia's first measurement (5-30s), and graceful degradation (sources_failed listed if timeout). 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 one solid paragraph but packs a lot of information efficiently. It is front-loaded with the core purpose. Could be slightly improved with bullet-point structure for easier scanning, but still very concise and dense.

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?

With no output schema, the description fully details the return values: summary block with specific fields, per-advisory detail, links, and alternative versions. It also covers partial failure behavior, making it complete for agent understanding.

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?

Input schema covers both parameters with clear descriptions (package name default, version optional). Schema description coverage is 100%, so the description adds no critical new meaning beyond the schema. Baseline of 3 is appropriate.

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 is a composite check for evaluating npm packages, listing specific data sources (deps.dev, bundlephobia) and returned fields. It distinguishes itself from siblings by being a one-call resource for safety/popularity/size questions.

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 explicitly tells when to use the tool: 'whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also states what the tool does NOT cover (non-npm ecosystems) and directs to an alternative (deps.dev:version) for other package managers.

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

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). An agent would struggle to select the appropriate tool due to ambiguous distinctions.

Naming Consistency3/5

Tool names are a mix of styles: some follow verb_noun (ask_pipeworx, forget), others use noun_verb (ecfs_docket_filings), and many are short or compound (bet_research, entity_profile). While the ecfs- prefix tools are consistent, the overall set lacks a uniform pattern.

Tool Count2/5

With 35 tools, the count is excessive for a server named after FCC ECFS, as only 4 tools are directly relevant to that domain. The inclusion of many general-purpose data tools makes the set feel bloated and misaligned with the server's apparent scope.

Completeness3/5

For the FCC ECFS domain, the tool set is incomplete (only 4 tools, lacking submission or deletion capabilities). However, as a general-purpose data query server, it covers many sources (SEC, FDA, patents, etc.). The name mismatch hurts the perceived completeness.

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