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Glama

Crypto 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?

The description discloses behavioral traits beyond the annotations, including partial failure degradation ("sources_failed will list it if it times out"), the 5-30s first-measurement latency, and the fact that the tool fans out across two external services. These details provide a richer mental model for runtime behavior.

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 information-dense and well-structured, opening with the concrete purpose and then layering usage, output fields, ecosystem constraints, and failure behavior. Every sentence earns its place without redundancy.

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?

Given the tool's multifaceted nature, the description covers all necessary context: use cases, exact return fields, ecosystem limitations, latency caveats, and graceful degradation. It is self-sufficient even without an output schema.

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?

The input schema already provides complete descriptions for both parameters (package name, optional version) with 100% coverage. The description does not add additional parameter-specific detail, so the baseline of 3 applies.

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 explicitly identifies the tool as a composite "should I add this npm package" check across deps.dev and bundlephobia, listing specific data sources and output fields. This clear verb-resource-purpose effectively distinguishes it from other research 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?

The description gives explicit usage triggers ("Use whenever an agent asks...") and an explicit exclusion for non-NPM ecosystems, pointing to an alternative ("deps.dev:version directly"). This is ideal guidance for choosing when to invoke the tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Crypto Feeds toolsInspect
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TDQS

A3.8/5.0
Disambiguation2/5

Several tools are nearly indistinguishable in role: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are all variants of the same router, with the beta version explicitly noted as currently identical to the stable one. The six Polymarket tools also overlap heavily around edge-finding, arbitrage, and fill-risk analysis, making misselection likely.

Naming Consistency3/5

Names are uniformly snake_case and groupable into prefixes like polymarket_* and pipeworx_*, but the set does not follow a consistent verb_noun convention. Noun-first names like entity_profile and recent_alerts sit alongside verb-first names like read_feed and validate_claim, and product-name suffixes such as ask_pipeworx_beta/grounded add further inconsistency.

Tool Count2/5

At 34 tools, the surface is well past the 25+ threshold and far broader than the 'Crypto Feeds' name suggests. The set spans feed reading, general data research, prediction markets, AI-brand visibility audits, npm dependency scanning, memory, and subscriptions, making it feel like a platform-wide dump rather than a focused MCP server.

Completeness3/5

The broad research workflow is well covered with query, grounded answer, deep research, entity profiles, comparisons, fact-checking, and entity resolution. However, feed functionality is read-only with no feed management, subscription types do not include crypto feeds despite the server name, and there is no dedicated tool for resolving the advertised pipeworx:// citation URIs.

Resources