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Glama

Youtube

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 (readOnlyHint, idempotentHint, destructiveHint) indicate safe behavior. The description adds context: partial failures degrade gracefully, bundlephobia's first measurement may take 5-30s, and sources_failed will list timeouts. 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?

The description is detailed but not overly verbose; each sentence adds information. However, it could be slightly more structured (e.g., summarizing return fields). Front-loading is good.

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 no output schema, the description thoroughly explains return values (summary block, per-advisory detail, links, alternative versions). It covers ecosystem scope, failure handling, and performance caveats. Exceptional completeness.

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 already describes both parameters. The description adds value by stating that version defaults to latest when omitted, and that scoped packages are accepted. This enhances understanding 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 is a composite check for evaluating npm packages, combining deps.dev and bundlephobia data. It distinguishes from siblings like 'scan_competitor_ai_presence' and mentions ecosystem limitations, making the purpose unambiguous.

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 says when to use (e.g., 'is X safe / popular / small') and when not to use (alternative tools for other ecosystems). Also describes graceful degradation and timeout behavior, guiding correct invocation.

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

A3.6/5.0
Disambiguation3/5

There is meaningful overlap among the ask_pipeworx, deep_research, validate_claim, and polymarket_* tools, but the descriptions do draw fairly clear boundaries between them. The five yt_* tools are distinct and easy to tell apart, though the unrelated Pipeworx cluster makes the overall set feel muddier than it should.

Naming Consistency3/5

Most tools use readable snake_case, and there are coherent prefixes like yt_ and polymarket_, but the set mixes bare verbs (remember, recall, forget, subscribe), noun-style names (entity_profile, deep_research), and API-like names (ask_pipeworx, generate_llms_txt). The pattern is not chaotic, but it is inconsistent across the set.

Tool Count2/5

36 tools is well above the typical well-scoped range, and the vast majority are unrelated to the server's stated 'Youtube' identity. The actual YouTube surface is only five tools, while 31 tools belong to a different Pipeworx/Polymarket domain.

Completeness2/5

For a YouTube-focused server, the yt_* tools cover search, channel info, video details, and comments, but miss obvious surfaces like playlists, transcripts, subscriptions, uploads, and video updates. The large non-YouTube tool collection does not fill these gaps; it only makes the server feel mis-scoped.

Resources