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Glama

Cricket

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

A4.5/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds behavioral details: fans out to three sources, fallback logic, soft-failure of USPTO, and output structure. Could elaborate more on response format but sufficient.

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 dense paragraph that efficiently packs a lot of information. Front-loaded with example queries. Could benefit from minor structuring but highly informative without unnecessary words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all major aspects: sources, fallback, input formats, output structure, and alternative tool. Missing details like empty result handling, but given no output schema, this is adequate.

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 extra value: for 'since' it gives examples and usage recommendation ('30d' or '1m'), for 'value' example formats, and clarifies only 'company' for type.

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 is extremely specific, using example queries to illustrate use cases and clearly stating it provides a change feed across multiple sources. It 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?

Provides clear when-to-use guidance: for temporal changes (news, filings, patents). Explicitly says when not to use (static profile, refer to entity_profile). Also 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.

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TDQS

A3.6/5.0
Disambiguation2/5

The set mixes several near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, current_matches and match_scores largely duplicate each other, and the Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) has fuzzy boundaries. Long descriptions help, but an agent would often need to read deeply to pick the right tool.

Naming Consistency3/5

Most names are lowercase snake_case, but conventions vary: some are verb_noun (ask_pipeworx, scan_dependency, compare_entities), some are noun phrases (current_matches, match_info, entity_profile), and there are mixed prefixes (pipeworx_*, polymarket_*, plain names). It is readable but not a coherent naming system.

Tool Count2/5

35 tools is heavy, and only four of them (current_matches, match_info, match_scores, search_players) relate to the server's apparent cricket purpose. The rest are a sprawling general-purpose data/research/prediction-market/utility toolkit, making the surface feel bloated and off-scope.

Completeness2/5

As a cricket server, the surface is shallow: it has live matches, scores, match info, and player search, but no player stats, batting/bowling figures, team profiles, series/schedules, or historical match data. The many unrelated tools do not fill these obvious cricket-domain gaps.

Resources