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Missouri License Offices

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 already declare readOnlyHint, openWorldHint, idempotentHint, and not destructive. The description adds rich behavioral details: fans out to SEC EDGAR, GDELT→GNews fallback, and USPTO (soft-fail until reactivated), with structured return format including citation URIs. No contradiction with annotations.

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 front-loaded with example queries, then explains the multi-source logic, parameters, and return format in a structured, non-repetitive way. Every sentence adds value, and the length is appropriate for the complexity.

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 lacking an output schema, the description thoroughly explains the return structure (changes grouped by source, total_changes count, citation URIs). It also covers limitations (USPTO soft-fail) and the fallback behavior. The tool is fully described for an agent to use 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?

Input schema has 100% description coverage. The description adds value by explaining the `since` parameter accepts ISO dates or relative shorthand (with examples like '7d', '3m'), and that `value` accepts ticker or CIK. It also provides guidance on typical usage for `since`.

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 clearly states the tool provides a change feed for a company in a recent time window, with example queries like "What's new with X" and "latest on Y." It explicitly distinguishes itself from the sibling tool entity_profile, which handles static profiles regardless of time window.

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 gives explicit guidance on when to use (for time-window change queries) and when not to (for static profiles, pointing to entity_profile). It also advises on the `since` parameter with examples like '30d' or '1m' for typical monitoring.

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
Disambiguation1/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route natural-language data questions, while entity_profile, recent_changes, compare_entities, and resolve_entity overlap around company data. An agent cannot reliably distinguish which retrieval entry point to choose.

Naming Consistency4/5

Tool names are almost uniformly lowercase snake_case and mostly follow a recognizable verb_noun or domain-prefixed pattern (ask_pipeworx*, polymarket_*, pipeworx_*, scan_*, recent_*, subscribe/unsubscribe). A few names like deep_research, entity_profile, and bet_research break the verb-first style, but there is no chaotic convention mixing.

Tool Count2/5

32 tools is too many for the apparent scope, and more importantly only one tool (mo_dmv_license_offices) matches the server name 'Missouri License Offices.' The other 31 tools form a general data-research, prediction-market, memory, and subscription platform that has little to do with the stated purpose.

Completeness4/5

Judged as the broad Pipeworx-style research platform the descriptions reveal, the surface is quite complete: simple and grounded querying, deep multi-source research, claim verification, entity resolution, profiles, comparisons, change feeds, discovery, subscriptions, alerts, and memory. For the literal Missouri license-office purpose, however, only the single lookup tool is present, which drags down completeness despite that tool being reasonably thorough.