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Glama

Wmata

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds specific behavioral details: parallel call, fallback logic, soft-fail for USPTO, and return format.

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 somewhat long but every sentence is informative. Front-loaded with purpose and usage, then details. Slightly verbose but not wasted.

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 mentions return structure (changes grouped by source, total_changes, citation URIs). Covers all key aspects for a multi-source query tool.

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 coverage is 100%, yet description adds value: explains fans-out behavior, date format examples ('2026-04-01', '7d'), and fallback logic for the 'since' parameter.

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 across SEC, GDELT/GNews, and USPTO sources within a time window, and distinguishes it from the sibling entity_profile tool.

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 usage examples ('What's new with X', etc.) and guidance on when to use entity_profile instead. Also explains fallback 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

A3.7/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but the server mixes WMATA transit tools (bus routes, rail predictions) with unrelated general-purpose tools (deep_research, polymarket_arbitrage). This broad scope can confuse an agent expecting a focused transit server.

Naming Consistency2/5

All names use snake_case, but there is no consistent verb_noun pattern. Many are noun_noun (bus_routes, rail_lines) or verb_properNoun (ask_pipeworx), and the naming conventions vary wildly across different domains (e.g., validate_claim vs entity_profile vs scan_dependency).

Tool Count2/5

41 tools is excessive for a WMATA transit server. The vast majority of tools (ask_pipeworx, deep_research, polymarket_*) are not related to WMATA, making the tool surface bloated and unfocused for its stated purpose.

Completeness3/5

The WMATA-specific tools cover core bus and rail operations (incidents, predictions, routes, stations). However, gaps like fare info and elevator status are missing. The non-transit tools are extensive but irrelevant to the server's apparent focus.

Resources