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Glama

Fdic

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

A4.8/5.0
Behavior5/5

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

Discloses partial failures, timing (5-30s first measurement), and graceful degradation. Annotations already declare readOnly and idempotent; description adds behavioral nuance without contradiction.

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?

Single paragraph is slightly long but every sentence is informative and front-loaded with core purpose. Minor improvement possible with structuring.

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?

No output schema, but description fully enumerates return fields (summary, advisories, links, alternatives) and covers edge cases (partial failures, timing).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline 3. Description adds value by clarifying scoped packages and default version behavior, exceeding schema docs.

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 it's a composite check for npm package evaluation covering license, advisories, version history, and bundle analysis. It distinguishes from siblings by specifying the exact use case.

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 (when agent asks safety/popularity/size questions) and limits to NPM ecosystem with alternative for other ecosystems.

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

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer questions over the same underlying data catalog, and the five polymarket_* tools plus bet_research all analyze prediction-market opportunities. Tools like entity_profile, recent_changes, compare_entities, and resolve_entity also blur together for company research.

Naming Consistency2/5

Names are all snake_case but follow no consistent convention: some are bare verbs (forget, recall, remember, subscribe), some are noun phrases (fdic_failures, entity_profile, pipeworx_trending), and some are verb_noun (fdic_get_institution, generate_llms_txt, validate_claim). Even the fdic_* family mixes noun-only and verb_noun styles.

Tool Count2/5

At 36 tools this exceeds the 25-tool threshold for 'too many.' The count is inflated by redundant meta-tools (three ask_pipeworx variants, discover_tools, suggest_questions, multiple polymarket scanners) and unrelated purpose tools (generate_llms_txt, scan_dependency, ai_visibility_check) that do not belong in an FDIC-named server.

Completeness3/5

The universal ask_pipeworx router gives broad data coverage for almost any factual question, so core lookups are unlikely to dead-end. However, the FDIC-specific surface is thin (only five tools, missing branch/geography/history data), and the server's actual scope is so broad and mixed that no single domain is fully covered end-to-end.

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