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Glama

Boe Uk

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

A4.7/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses composite behavior, partial failure handling (sources_failed), and a critical latency caveat (bundlephobia first measurement 5-30s). It also lists the exact return fields, which is valuable given no output schema. 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?

Every sentence earns its place: purpose, use-case, return payload, ecosystem limitation, and failure behavior. Information is front-loaded and well-structured; despite length, it remains scannable and efficient.

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?

The description is remarkably complete for a composite tool with no output schema: it names both upstream services and their contribution, enumerates the summary block fields, describes advisory detail and links, explains ecosystem scope, and warns about latency and graceful degradation. This gives the agent a full mental model.

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% with both 'package' and 'version' already described, so the baseline is 3. The description adds ecosystem context (npm-only) and hints at version history in deps.dev, but doesn't substantially expand parameter meaning beyond 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 opens with a specific composite verb+resource: 'should I add this npm package to my project' check that fans out across deps.dev and bundlephobia. It clearly distinguishes from siblings by being a one-call aggregate and npm-specific, unlike other tools like compare_entities or validate_claim.

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: 'Use whenever an agent asks "is X safe / popular / small" or "what does adding lodash cost me"'. It also provides an exclusion for non-npm ecosystems, directing to deps.dev:version directly, and implies alternatives through that scoping.

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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same 5,596 tools, with beta currently identical to stable. The suite of six polymarket_* tools and the ai_visibility_check / scan_competitor_ai_presence pair also risk misselection, despite detailed descriptions.

Naming Consistency4/5

The overwhelming majority of tools follow a clear snake_case verb_noun pattern (ask_pipeworx, resolve_entity, validate_claim, subscribe). Minor deviations exist for bare data endpoints like bank_rate, sonia, and eur_gbp, but these are still predictable and readable.

Tool Count2/5

37 tools is heavy for a single server, beyond the 25-tool threshold indicating a bloated surface. While the broad research scope justifies some size, many tools are variants or wrappers of the same underlying router, which pads the count and creates cognitive load.

Completeness4/5

For a research and data gateway, the surface covers lookups, grounded answers, deep research, claim verification, entity resolution, prediction-market analysis, BoE series, memory, and subscriptions. Minor gaps exist—such as no direct update for stored entities and BoE series limited to four convenience wrappers—but agents can work around these.

Resources