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Glama

Texas DMV

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

A4.8/5.0
Behavior5/5

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

Annotations indicate readOnly, openWorld, idempotent, non-destructive. The description adds valuable behavioral details: fans out to multiple sources, fallback behavior, soft-fail for USPTO, and date shorthand acceptance. 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 detailed but front-loaded with examples. Every sentence contributes useful information. Slightly verbose but well-structured.

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?

No output schema, but description describes return structure (changes[] grouped by source, total_changes, citation URIs). Covers multiple data sources, fallback, and date formats. Complete for the tool's complexity.

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 covers all 3 parameters with descriptions and enum. Description reinforces 'since' shorthand and provides usage recommendation ('30d' or '1m'). Adds extra context beyond schema, earning a score above baseline.

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 it provides a change feed for a company in a time window, with example queries and explicit distinction from sibling tool entity_profile. The verb 'change feed' and listing of sources (SEC, GDELT, USPTO) make the purpose unambiguous.

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 'when to use' with example queries and 'when not to use' with a direct instruction to use entity_profile for static profiles. It also explains fallback logic between GDELT and GNews.

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

A3.5/5.0
Disambiguation2/5

Several tools occupy nearly interchangeable roles: ask_pipeworx and ask_pipeworx_beta are explicitly identical, while ask_pipeworx_grounded, deep_research, and validate_claim all route similar factual queries. discover_tools/suggest_questions and bet_research/polymarket_edges similarly overlap, so an agent needs to read long descriptions to avoid misselection.

Naming Consistency3/5

All names are readable lowercase snake_case, but the conventions are mixed: imperative verb_noun names (list_subscriptions, validate_claim) sit alongside noun phrases (polymarket_edges, recent_alerts), bare verbs (forget, subscribe), and variant suffixes (ask_pipeworx_beta/grounded). It is not chaotic, but there is no single predictable naming pattern.

Tool Count2/5

Thirty-two tools is far more than the apparent Texas DMV scope supports: only tx_dmv_vehicle_registrations is DMV-related, and the rest are Pipeworx platform, prediction-market, memory, and unrelated utility tools. The count is excessive for the server's stated name and purpose.

Completeness1/5

The Texas DMV surface is severely incomplete: a single statewide registration-count tool covering fiscal years 2001-2021, with no title/registration transactions, VIN lookup, driver services, county/ZIP breakdowns, or current data. The tool's own description references a California DMV companion that is not present, leaving obvious gaps for any realistic DMV workflow.

Resources