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Acled

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

A5/5.0
Behavior5/5

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

Annotations already indicate safe read-only, non-destructive behavior. Description adds significant context: fans out across multiple sources, explains fallback logic, soft-fail for USPTO, and return structure with citations.

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?

Front-loaded with use cases, then explains sources and parameters, then contrasts with alternative. Every sentence adds value, no fluff.

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?

Despite no output schema, description details return structure. The complexity of multiple data sources and fallback is fully covered. The description is complete for an agent to select and invoke this tool correctly.

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%, but description adds examples and guidance (e.g., 'Use 30d or 1m for typical monitoring'), clarifies value accepts ticker or CIK, and explains since 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?

The description specifies it is a change feed for a company with examples like 'What's new with X' and distinguishes from sibling tool 'entity_profile' which covers static profiles.

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?

Explicitly states when to use (e.g., updates, news, changes) and when to use entity_profile instead, plus fallback behavior for data sources.

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

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) which all serve similar data retrieval needs. Additionally, entity_profile, compare_entities, and recent_changes overlap in providing company information. Polymarket tools also have overlapping analysis functions. This can cause confusion for agents.

Naming Consistency2/5

Tool names are inconsistent in style and convention. Some use underscores (ai_visibility_check, ask_pipeworx), others are single words (forget, recall), and many lack a clear verb_noun pattern (pipeworx_feedback, polymarket_edges). This mixture of naming conventions reduces predictability.

Tool Count3/5

With 32 tools, the count is on the higher side but appropriate given the broad scope covering multiple domains (Pipeworx data, Polymarket betting, ACLED events, npm scanning, memory, etc.). However, some areas have only one or two tools, which feels sparse, and the overall set could be trimmed or better organized.

Completeness3/5

The tool set covers many domains but has notable gaps. For ACLED, only search and count tools exist without any update/delete capabilities. For Pipeworx, the tools are heavily read-focused with no apparent write operations. The broad scope makes completeness hard to assess, but some obvious lifecycle operations are missing.

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