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Glama

Nhl

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Description goes beyond annotations by detailing the fan-out to multiple sources, fallback mechanisms, and return structure (changes grouped by source, total_changes, citation URIs). 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 concise yet comprehensive, with a clear front-loading of example queries, followed by detailed source behavior and parameter explanations. No unnecessary words.

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 has 3 required params, no output schema, and annotations present, the description fully covers inputs, behavior, return structure, and sibling differentiation. An agent can confidently invoke this tool.

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 coverage is 100%, but description adds value by explaining `since` formats (ISO date or relative shorthand) with examples, suggesting '30d' for monitoring, and specifying that `value` accepts ticker or CIK. This enhances the schema descriptions.

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 explicitly states the tool's purpose: providing a change feed for a company in the last N days/weeks/months, with specific examples like 'What's new with X' and 'updates on Acme'. It distinguishes from sibling tool 'entity_profile' by noting when to use that instead.

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?

Gives explicit when-to-use guidance (queries about recent changes, latest news) and when-not-to-use (static profiles should use entity_profile). Also explains fallback behavior (GDELT→GNews) and soft-fail for USPTO.

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

All Nhl toolsInspect
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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