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Glama

Football Data

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. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Goes well beyond annotations by detailing the fan-out to multiple sources, the GDELT-to-GNews fallback, soft-failure for USPTO due to API sunset, and the exact return structure (changes grouped by source, total count, 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough and well-structured, starting with intuitive query examples. Every sentence adds useful information, though it is somewhat lengthy. It is front-loaded with examples and clearly separates the tool's purpose from the sibling reference.

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?

For a tool with no output schema, the description fully explains what is returned (changes array grouped by source, total_changes count, pipeworx URIs). It covers multiple data sources, fallback behavior, window handling, and provides an alternative tool for static profiles. Complete enough for an AI agent to use correctly.

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?

Input schema already has descriptions for all parameters (100% coverage). The description adds value by clarifying the `since` parameter's accepted formats (ISO date or relative shorthand) and giving usage examples. It also notes that `type` only supports 'company' currently.

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?

Description clearly states the tool provides a change feed for a company over a time window, covering SEC filings, news, and patents. It uses concrete examples and explicitly distinguishes from the sibling tool entity_profile by saying 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?

Provides numerous example queries to signal appropriate usage scenarios. Includes explicit guidance on when not to use it (use entity_profile for static profile) and explains fallback behavior between GDELT and GNews.

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

All Football Data toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route factual questions to data sources, making it hard to pick the right one. The server name 'Football Data' is misleading because most tools are unrelated to football, and the few football tools are distinct but swamped by the general-purpose data platform.

Naming Consistency3/5

There is a mix of conventions: some tools follow verb_noun (get_competition_matches, list_competitions, scan_dependency, resolve_entity), but many are noun phrases (entity_profile, polymarket_arbitrage, pipeworx_trending, deep_research) or verb-only (remember, forget, subscribe). The use of the 'pipeworx' and 'polymarket' prefixes is inconsistent across the set.

Tool Count2/5

36 tools is excessive for a server named 'Football Data', especially since only 5 tools are football-specific. The majority are general-purpose data/research tools that belong in a separate server, inflating the count and diluting the server's focus.

Completeness2/5

The football surface is incomplete: there are tools for listing competitions, getting matches, standings, and team details, but no player stats, match events, head-to-head records, or live score details. While the incidental general-purpose tools are extensive, they don't fill the gaps in the server's stated football domain.

Resources