Fintech Intel

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

Name	Required	Description	Default
`package`	Yes	npm package name. Scoped packages (e.g. "@types/node") are accepted.
`version`	No	Specific 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.

AddedJun 3, 2026

TDQS

A4.7/5.0

Behavior5/5

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

The description adds significant behavioral context beyond the readOnly/openWorld/idempotent annotations: it describes composite data sourcing, graceful partial failures, timeout behavior, and a 5-30s first-measurement caveat. 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.

Conciseness5/5

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

The description is efficient and front-loaded with the core purpose. Each sentence earns its place: purpose, use cases, return fields, ecosystem limits, and performance caveat. Despite moderate length, it is tightly structured with no fluff.

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?

For a complex composite tool, the description is remarkably complete. It lists the return summary fields, alternative version details, ecosystem restrictions, failure behavior, and performance expectations. Since there is no output schema, this explanatory text is essential and thorough.

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 adds minimal parameter meaning beyond the schema—'package' and 'version' are already described with defaults and examples. It does reinforce npm-only scope but provides no extra syntax or format details.

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 verb+resource+scope: 'Composite should I add this npm package to my project check' that fans out across deps.dev and bundlephobia. It clearly distinguishes from siblings by focusing on npm dependency evaluation, contrasting with other analysis tools.

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 use cases are given: 'Use whenever an agent asks is X safe / popular / small' or 'what does adding lodash cost me'. It also provides an exclusion and alternative: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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

A3.7/5.0

Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx and ask_pipeworx_beta are explicitly identical, discover_tools/suggest_questions/pipeworx_trending all serve discovery, and multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) find opportunities. Descriptions help but an agent could easily pick the wrong data-query tool.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern, mostly verb_noun (ask_pipeworx, resolve_entity, validate_claim) and prefix groups (fintech_*, polymarket_*). Minor deviations like entity_profile or recent_changes are noun phrases but still readable and predictable.

Tool Count2/5

34 tools is excessive for a coherent set; the server appears to bundle a general-purpose data research platform, prediction-market analysis, memory, and subscriptions under one 'Fintech Intel' name. Many meta-tools could be split into separate servers, and the count burdens an agent with unnecessary choices.

Completeness4/5

The tool surface covers the fintech/data-intel domain thoroughly: SEC filings, FDIC, FDA, economic data, real estate, prediction markets, monitoring subscriptions, and memory. Gaps are minor—e.g., no direct tool for historical stock charts, but the router (ask_pipeworx) handles such queries.

Resources

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