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Glama

Finance Feeds

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

A4.7/5.0
Behavior5/5

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

Annotations already signal read-only, non-destructive, open-world, and idempotent behavior. The description adds valuable behavioral context beyond annotations: partial failure handling (sources_failed), potentially slow first bundlephobia measurement (5-30s), and ecosystem restriction (NPM only). No contradictions 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: purpose, fan-out sources, when-to-use, return summary fields, ecosystem scope, and failure behavior. The key phrase 'in ONE call' is front-loaded, and no fluff or repetition exists.

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 sufficiently enumerates return contents (summary block fields, advisory details, links, alternative versions), clearly defines ecosystem scope, and explains partial failure behavior. This is complete for the complexity of a composite tool.

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% and both parameters have descriptions, so the baseline is 3. The description adds little beyond the schema: it confirms package is an npm package and version defaults to latest, both already stated in the schema. No additional format constraints or edge-case guidance is provided.

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 states a specific composite purpose: a one-call 'should I add this npm package' check across deps.dev and bundlephobia. It clearly identifies the resource (npm dependency) and distinguishes the tool from siblings by emphasizing the one-call composite nature and returning package health/size metrics.

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?

Explicit usage guidance is provided: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also gives an alternative for non-NPM ecosystems (deps.dev:version directly), making the when/where boundaries clear.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but there is overlap among query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and several Polymarket-specific tools. Descriptions help differentiate, but some confusion is possible.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern (e.g., ai_visibility_check, compare_entities, validate_claim). No mixing of conventions, and naming is predictable.

Tool Count3/5

33 tools is a high count for a finance server, including many meta-tools (memory, subscriptions, feedback) that are not finance-specific. The core finance set is reasonable, but the overall surface feels heavy.

Completeness3/5

The server covers many data sources (SEC, FRED, FDA, etc.) but lacks direct stock quotes or fundamental CRUD operations. Some areas (e.g., Polymarket, AI visibility) are over-represented, leaving gaps in core finance tasks.

Resources