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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 confirm read-only, idempotent, non-destructive nature. Description adds behavioral details: multi-source fan-out, fallback logic, USPTO soft-fail, and return structure (changes grouped by source, total_changes, citation URIs). No contradiction.

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?

Description is thorough but slightly verbose. However, every part serves a purpose: examples, source list, fallback, parameter hints, sibling reference. Front-loaded with examples, making it easy to scan.

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 covers return format and sources comprehensively. Addresses complexity of multi-source feed with fallback and deprecation note. No missing context for agent to use 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 has 100% coverage, but description adds value by explaining since format (ISO vs relative shorthand) with examples, and clarifying value accepts ticker or CIK. Significantly enhances parameter understanding beyond schema.

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 explicitly states 'change feed for a company' and provides concrete usage examples ('what's new with X'). It differentiates from sibling entity_profile by stating 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 clear when-to-use guidance via example queries and explicit when-not-to-use reference to entity_profile. Mentions fallback behavior between GDELT and GNews, aiding decision-making.

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

The set contains several near-duplicate entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) plus a dense family of polymarket_* and chemical lookup tools whose boundaries are subtle. An agent can easily pick the wrong router variant, edge-scanning tool, or chemical search tool.

Naming Consistency3/5

Snake_case is used throughout, but naming style is mixed: some tools are verb-first (query, resolve_entity, validate_claim), some are noun/domain-first (entity_profile, recent_changes, polymarket_arbitrage), and the ask_pipeworx variants use suffixes instead of a consistent verb pattern. It is readable, but there is no predictable convention beyond snake_case.

Tool Count2/5

34 tools is well past the comfortable range, and the count is especially hard to justify because they span unrelated domains: chemistry lookups, a Pipeworx data gateway, prediction-market analytics, memory, subscriptions, AI-visibility checks, and npm dependency scanning. Many of these have no obvious connection to the 'Mychem' name or to each other.

Completeness4/5

Within each contained workflow the surface is fairly complete: chemistry has search/fetch/metadata, Pipeworx has lookup/research/discovery/validation, subscriptions and memory both have lifecycle coverage, and the Polymarket family covers research, edge detection, persistence, and fill risk. Some minor gaps exist (no general web retrieval or execution/trading action), but agents can work around them.