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Glama

Gnews

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?

Discloses parallel calls, fallback logic, soft-fail for USPTO, and output format. Annotations already indicate safety, but description adds behavioral depth.

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?

One efficient paragraph front-loading examples and purpose. Dense but no wasted words. Minor room for tighter phrasing.

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?

Covers purpose, usage, parameters, return format, and alternative tool. No gaps given schema coverage and annotations.

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 already describes all 3 parameters; description adds guidance on since formats and typical use, plus clarifies type and value formats.

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?

Clear verb+resource: 'change feed for a company in the last N days/weeks/months'. Examples ground the tool. Explicitly distinguishes from sibling 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?

States when to use and when not ('use entity_profile instead'), gives parameter suggestions ('Use "30d" or "1m"'), and explains fallback behavior.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct, well-described purposes, but there is some overlap, especially among prediction market tools (bet_research, polymarket_arbitrage, etc.) and between ask_pipeworx and ask_pipeworx_grounded. Agents might occasionally select the wrong tool without careful reading.

Naming Consistency3/5

Tool names follow a mix of snake_case and camelCase (e.g., ai_visibility_check vs discover_tools). Some names are descriptive but inconsistent in style (subscribe, unsubscribe, list_subscriptions). Pattern is not uniform.

Tool Count3/5

With 32 tools, the server covers many domains (news, financials, prediction markets, entity resolution, memory). While each tool has a justification, the count feels heavy for a single server, and some tools could be consolidated.

Completeness3/5

The tool set spans a wide range of data sources and operations, but there are notable gaps. For news, only search and top headlines exist without advanced filtering. Prediction markets lack order placement tools. The broad scope means depth is sacrificed in some areas.

Resources