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Glama

Thesportsdb

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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds valuable detail about multi-source fan-out, fallback logic (GDELT→GNews), and soft-failure for USPTO, which goes beyond 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 comprehensive but slightly verbose with multiple example queries. It is well-structured and front-loaded with purpose, but could be trimmed without losing value.

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?

The tool is complex with multiple sources and fallback logic. The description fully explains behavior, parameters, and return structure (grouped changes, total count, citation URIs). No output schema, but description provides sufficient info.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters with descriptions. The description adds meaning by explaining 'since' accepts ISO or relative formats and suggests '30d'/'1m', and that 'value' can be ticker or CIK. This enhances 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 the tool provides a 'change feed for a company in the last N days/weeks/months'. It gives numerous example queries and explicitly distinguishes itself from the sibling tool entity_profile.

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?

The description includes explicit usage context ('What's new with X', 'latest on Y') and an alternative ('Use entity_profile instead when you want the static profile'). It also advises on typical 'since' values.

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

All Thesportsdb toolsInspect
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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are domain-distinct and have detailed descriptions, but the ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio overlaps heavily (beta is currently identical to the stable router), and several Polymarket discovery tools (polymarket_edges, bet_research) serve similar purposes. The sports tools themselves are clearly separated by resource (player/team/league/event).

Naming Consistency4/5

All 42 tools use snake_case consistently and most follow a verb_noun pattern (get_player, list_leagues, search_teams), but a significant subset inverts to noun-first naming (team_events_next, league_table, polymarket_edges) or uses ad hoc phrases (events_by_day, search_within). The convention is predictable overall, though not fully uniform.

Tool Count2/5

42 tools exceeds the heavy-set threshold, and the bloat is worse because roughly 30 of them are generic Pipeworx data, prediction-market, or meta tools unrelated to the server's stated TheSportsDB purpose. A focused sports server would be well-served by 10-15 tools.

Completeness3/5

The TheSportsDB subset covers core lookups (sports, leagues, teams, players, schedules, results, standings) but lacks obvious sports-domain operations like player statistics, detailed match/event info, rosters, or historical data. The unrelated Pipeworx tools add broad but misplaced coverage, leaving notable gaps for a server named Thesportsdb.

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