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Glama

Revternal

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. Description adds value by detailing the multi-source fan-out (SEC, GDELT/GNews, USPTO), fallback logic, and return structure. No contradictions.

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?

Front-loaded with user-facing examples, but description is somewhat long. Every sentence adds information, but could be slightly tightened without losing clarity.

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?

Given the complexity (multiple sources, fallbacks, soft-failures) and absence of output schema, the description covers return structure (grouped changes, counts, citation URIs) and edge cases thoroughly.

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 context: examples for `since` (ISO and relative shorthands like '30d', '3m'), and clarification that `value` accepts tickers or CIKs. This goes beyond the schema's baseline descriptions.

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?

Description clearly states it provides a change feed for a company, with specific query examples ('What's new with X', 'latest on Y'). It explicitly distinguishes from the sibling tool entity_profile by noting when to use the static profile instead.

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 explicit when-to-use examples and mentions when to use entity_profile as an alternative. Also explains fallback behavior between GDELT and GNews, and soft-failure for patents.

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

All Revternal toolsInspect
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TDQS

A4/5.0
Disambiguation3/5

Several tool families (ask_pipeworx variants, polymarket analysis tools) have overlapping purposes, which could confuse an agent. However, descriptions are detailed and help differentiate them in most cases.

Naming Consistency4/5

All tool names use snake_case and are descriptive, but prefixes vary (ask_, polymarket_, revternal_, etc.) and some verbs are standalone (forget, recall, remember), breaking a strict verb_noun pattern.

Tool Count3/5

35 tools is on the high side, but the scope is broad (data research, prediction markets, developer intel). Some redundancy (multiple ask_pipeworx modes) could be consolidated, making the set feel slightly heavy.

Completeness4/5

The tool set covers core CRUD for data, memory, subscriptions, and analytics. Minor gaps exist (e.g., no file upload, limited account management), but the domain is well-served.

Resources