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Glama

Nba

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
Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral context: partial failure handling (bundlephobia timeout up to 30s, sources_failed listed), and the return format (summary block with specific fields, advisories, links, alternatives). 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?

The description is dense but well-structured, front-loading the main purpose and then adding behavioral and usage details. Every sentence provides necessary information 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 complexity (composite check across multiple sources), lack of output schema, and rich annotations, the description is comprehensive. It explains the return format (summary block with specific fields, advisories, links, alternative versions), failure modes (graceful partial failures), and ecosystem limitations.

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 description coverage is 100% (both package and version described in schema). The description adds context beyond schema: scoped packages are accepted, and version defaults to latest published. This extra guidance improves parameter understanding.

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 data. It uses specific verbs and resources ('scan_dependency', 'fans out across deps.dev... and bundlephobia'), and distinguishes from siblings by noting 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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 guidance on alternatives: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under 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

A3.5/5.0
Disambiguation2/5

Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research all provide factual lookups with similar boundaries. NBA-specific tools are mixed with generic Pipeworx utilities, making it hard for an agent to distinguish when to use which.

Naming Consistency2/5

Tool names mix conventions: snake_case (ai_visibility_check), camelCase (generate_llms_txt), and inconsistent verb usage (ask_pipeworx vs. search_players). No clear pattern emerges across the set.

Tool Count2/5

With 34 tools, the server is overpopulated, especially given its 'Nba' focus. Many tools (e.g., remember, forget, list_subscriptions) are generic utilities unrelated to NBA data, inflating the count beyond a coherent scope.

Completeness2/5

For an NBA server, tools are severely incomplete: missing player stats, team stats, season standings, and live game details. While the Pipeworx data tools are extensive, they diverge from the server's apparent purpose, leaving clear gaps.

Resources