Skip to main content
Glama

Nhl

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?

Discloses partial failure graceful degradation, potential 5-30s delay for bundlephobia first measurement, and that sources_failed will list timeout. Annotations already indicate readOnly, idempotent, non-destructive; 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?

Single paragraph with dense, useful information. Front-loaded with purpose, then usage, then details. Slightly long but 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?

No output schema, but description details return summary fields, per-advisory detail, links, and alternative versions. Also explains error behavior. Very 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 has 100% coverage; description adds useful context: accepts scoped packages, defaults to latest version when omitted. Adds value 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 clearly states the tool's purpose: a composite check for adding an npm package, summarizing license, advisories, size, and tree-shake support. It distinguishes from siblings by focusing on npm dependencies, unlike other research/scan tools.

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?

Explicit guidance on when to use: when asked 'is X safe/popular/small' or 'what does adding lodash cost'. Also notes limitations: only npm ecosystem and alternative for other ecosystems.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Nhl toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The set contains multiple near-overlapping query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools) and a cluster of prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) with fuzzy boundaries. ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, making misselection likely.

Naming Consistency2/5

Naming is mixed across clusters: get_* for NHL tools, ask_pipeworx* for queries, polymarket_* for prediction markets, bare verbs (remember, recall, forget), and noun_verb phrases (entity_profile, recent_changes, scan_dependency). Each cluster is internally consistent, but there is no unifying pattern across the server.

Tool Count1/5

35 tools on a server named 'Nhl' is an extreme mismatch: only 4 tools actually relate to NHL data, while the other 31 are a general-purpose Pipeworx data/research/prediction-market platform. The set is not well-scoped for its apparent purpose, and even as a general data server it feels like a grab bag of unrelated capabilities.

Completeness2/5

For the NHL domain the surface is thin: player, schedule, scores, and standings, but no team info, roster lookup, player search by name, or game/play-by-play details. The Pipeworx side is more complete but fills the server with functionality unrelated to the NHL branding, so the overall surface is fragmented and leaves obvious domain gaps.

Resources