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Glama

The Graph

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?

Annotations already declare readOnly, idempotent, non-destructive. Description adds significant behavioral details: composite call structure, fan-out to two services, partial failure handling, timeout specifics (5-30s), and return field list. 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?

Description is long but every sentence adds value. Front-loaded with main purpose. Could be slightly more concise but highly efficient given the complexity.

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?

Despite lacking an output schema, the description fully enumerates the return fields, handles edge cases (partial failures, timeout), and covers all important aspects. Highly complete.

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 is 3. Description adds that scoped packages are accepted and version defaults to latest, which adds helpful context beyond the schema.

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 clearly states it's a composite check for npm packages covering license, advisories, version history, and bundlephobia info. It distinguishes itself from siblings by specifying the exact use case and ecosystem scope.

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 states when to use ('is X safe / popular / small' or 'what does adding lodash cost me'), specifies NPM-only scope, and mentions graceful degradation for partial failures. Provides clear guidance on context.

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 overlap heavily: ask_pipeworx and ask_pipeworx_beta are described as currently identical, and deep_research/ask_pipeworx plus the many polymarket_* tools have adjacent intents that are easy to confuse. scan_competitor_ai_presence also directly wraps ai_visibility_check, so an agent can easily select the wrong tool for the same task.

Naming Consistency3/5

Names are consistently snake_case, but the convention is mixed: some are verb-first (query_subgraph, introspect_schema, validate_claim), some are noun-first (entity_profile, polymarket_edges, pipeworx_trending), and some are bare verbs (remember, recall, forget). The repeated prefixes help, but there is no single predictable naming pattern across the set.

Tool Count2/5

With 33 tools, this exceeds a reasonable single-server footprint, and the scope sprawls across data routing, prediction markets, AI visibility, npm dependencies, llms.txt generation, memory, and subscriptions. Many tools feel like separate mini-applications rather than one coherent toolkit for The Graph.

Completeness3/5

The Pipeworx data-research and prediction-market side is well covered with lookup, grounded answers, research, comparison, profiles, claims, subscriptions, and memory. However, the server's apparent namesake, The Graph, is thin: only query_subgraph and introspect_schema exist, with no subgraph discovery, status, or management tools.

Resources