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Glama

Imei

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

A4.6/5.0
Behavior5/5

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

The description adds rich behavioral context beyond annotations: partial failures, bundlephobia first-measure delay of 5-30 seconds, sources_failed list, and graceful degradation. Annotations indicate readOnly, openWorld, idempotent, non-destructive, and no contradiction.

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?

The description is packed with information, well-organized, and front-loads the main purpose. While slightly long, every sentence serves a purpose, making it efficient for an AI agent.

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 no output schema, the description details the return structure: summary block fields, per-advisory detail, links, and alternative versions. This is thorough for a composite tool with two services, covering all necessary context.

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 parameters described. The description does not add significant value beyond the schema; it essentially restates the parameter descriptions. Baseline 3 is appropriate.

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 combining deps.dev and bundlephobia, with specific verb 'check' and resource 'npm package'. It distinguishes itself from siblings by focusing on npm dependency analysis, unlike other tools like bet_research or compare_entities.

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 explicitly says 'Use whenever an agent asks 'is X safe / popular / small' or 'what does adding lodash cost me'. It also specifies the NPM ecosystem only and mentions partial fallback and alternatives for other ecosystems (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

A4/5.0
Disambiguation3/5

Many tools are distinct in purpose, but there is significant overlap among the data-query tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same underlying 5,756 tools. The descriptions help differentiate them, but an agent could easily select the wrong router for a given question.

Naming Consistency2/5

The naming is a mix of verb_noun (ask_pipeworx, resolve_entity, validate_claim), noun-only/product names (bet_research, compare_entities, entity_profile, deep_research), and generic terms (remember, recall, forget, subscribe, unsubscribe). Some tools have prefixes like polymarket_* and pipeworx_*, but ask_pipeworx variants deviate from the pattern. No consistent convention across the set.

Tool Count3/5

33 tools is on the heavy side but arguably justified by the broad domain (SEC data, FDA, prediction markets, subscriptions, memory, AI visibility, npm scanning, IMEI validation). However, several tools feel like they belong to separate concerns (IMEI check, npm dependency scanning, llms.txt generation), making the set feel unfocused.

Completeness4/5

The core Pipeworx query surface is well-covered: routing, grounded answers, deep research, entity profiles, comparisons, claim verification, and tool discovery. Subscription CRUD is complete (subscribe, recent_alerts, unsubscribe, list_subscriptions), and memory tools are complete (remember, recall, forget). Minor gaps: no direct tool for editing a stored memory, and IMEI tools are minimal (validate + check digit only).

Resources