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Glama

Fara

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

A4.5/5.0
Behavior5/5

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

Annotations already indicate safe read-only operation. Description adds important behavioral details: partial failures, bundlephobia first measurement delay (5-30s), and sources_failed listing. No contradictions.

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?

Three sentences, front-loaded with purpose and use cases, followed by edge cases and ecosystem scope. Efficient but slightly verbose in the middle sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description clearly describes return structure (summary block, advisories, links, alternative versions) and failure behavior. Comprehensive for a tool with good annotations.

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 covers both parameters fully (100% coverage). Description adds no additional semantic value beyond what the schema already provides for package and version.

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?

Clearly states it performs a composite npm package check using deps.dev and bundlephobia. Distinguishes from sibling tools, which are unrelated to dependency scanning.

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 says when to use (questions about safety, popularity, size) and when not (non-NPM ecosystems, which are handled by other tools). Provides alternatives.

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

Several tools have heavily overlapping purposes: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk) all surface betting opportunities with only subtle differences. The line between ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools is also fuzzy, making misselection likely.

Naming Consistency3/5

All tool names use snake_case and are readable, but naming conventions are mixed: many follow verb_noun (ask_pipeworx, discover_tools, list_foreign_principals), while others use noun-first or adjective_noun (entity_profile, polymarket_arbitrage, recent_alerts). Several tools share the 'pipeworx_' or 'polymarket_' prefix without that prefix meaning a consistent action type.

Tool Count2/5

34 tools is high for a single MCP server, and the scope spans unrelated domains (general data lookup, prediction-market analytics, FARA registrations, memory, subscriptions, AI-visibility monitoring). This feels like several servers merged rather than one cohesive set; many tools could be split into focused modules without losing functionality.

Completeness4/5

Core workflows are well covered: flexible data querying (ask_pipeworx, grounded, deep_research), entity resolution, FARA search/document retrieval, memory CRUD, subscription lifecycle, and claim validation. There are minor gaps such as no direct tool for single-source parameterized queries (everything routes through the universal router) and no account/profile management, but agents can complete the main advertised tasks.

Resources