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Glama

geo

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. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, etc.), the description discloses multi-source fan-out, fallback behavior due to rate limits/5xx, the PatentsView API sunset causing soft-fail, and the exact return structure (changes[] grouped by source, total_changes, pipeworx:// URIs). No annotation contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

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

The description is dense but every sentence contributes: user intents, source fan-out, parameter semantics, return structure, and alternative tool. It is front-loaded with examples and structured logically, earning its length.

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 complex multi-source tool with no output schema, the description covers purpose, parameters, fallback behavior, return shape, and alternatives. It is fully self-contained and leaves no significant gaps for an agent to select and invoke it 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?

Schema coverage is 100%, so baseline is 3. The description adds extra value by explaining the `since` parameter's ISO/relative shorthand with examples and recommending '30d'/'1m' for typical monitoring. This goes beyond schema descriptions, warranting a 4.

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 opens with concrete user intents ('What's new with X') and clearly defines the tool as a change feed for a company over a time window, fanning out to SEC, GDELT/GNews, and USPTO. It explicitly distinguishes itself from the sibling tool entity_profile, making its purpose unmistakable.

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 provides explicit usage examples ('latest on Y', 'updates on Acme') and direct guidance to use entity_profile instead for static profiles regardless of window. It also explains the fallback logic between GDELT and GNews, giving clear when-to-use context.

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
Disambiguation2/5

Multiple tools serve overlapping purposes, especially ask_pipeworx and ask_pipeworx_beta (explicitly identical) and ask_pipeworx_grounded/deep_research/validate_claim for fact retrieval. Even with detailed descriptions, an agent could easily misselect among data-query tools or among the five Polymarket analysis tools.

Naming Consistency3/5

Tool names mix verb-first (ask_pipeworx, compare_entities), noun-first (polymarket_edges, entity_profile), and single-word verbs (geocode, forget), with no strict verb_noun pattern. However, all names are snake_case and mostly descriptive, so the inconsistency is moderate.

Tool Count2/5

36 tools is far beyond the typical well-scoped range of 3-15, and the feature set spans research, memory, subscriptions, prediction markets, and geo utilities. Many tools are meta-tools (discover_tools, suggest_questions) that could be consolidated, making the surface feel bloated.

Completeness4/5

For the apparently broad domain of data research and prediction markets, the toolset covers most needs with parallel research, grounding, claim verification, memory, and subscription lifecycle. Minor gaps exist—like a direct way to fetch arbitrary raw data or a unified list of all tools—but agents can generally work around them.

Resources