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Glama

Connecticut 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

A4.9/5.0
Behavior5/5

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

The description details the fan-out to multiple sources, fallback mechanism, and return structure, adding significant context beyond the annotations which already mark it as read-only and idempotent.

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?

The description is well-structured with examples and guidance, though slightly long; every sentence is valuable, but could be tightened slightly.

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?

Covers purpose, usage, behavior, parameters, and return structure despite no output schema; explains fallback logic and soft-fail, and references sibling tool.

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 the description adds extra context for 'since' (format and typical usage), 'value' (ticker or CIK), and 'type' (only company supported).

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, with example queries. It distinguishes from the sibling tool entity_profile, which is for 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?

Explicit guidance on when to use (what's new queries) and when not to (static profile needs), with a direct reference to entity_profile as the alternative.

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

All Connecticut DMV toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx and ask_pipeworx_beta are explicitly the same right now, while ai_visibility_check and scan_competitor_ai_presence overlap heavily. The only DMV-specific tool is otherwise buried among generic research, prediction-market, memory, and subscription tools that an agent would struggle to separate.

Naming Consistency3/5

Most tools use snake_case, but conventions are mixed: some are verb_noun (list_subscriptions, generate_llms_txt), some are bare verbs (remember, forget), some are brand-prefixed nouns (pipeworx_trending, polymarket_edges), and ask_pipeworx lacks a conventional verb pattern. Still readable, but not a cohesive naming scheme.

Tool Count1/5

32 tools is already heavy, but nearly all of them are unrelated to the stated Connecticut DMV scope. The server would be better served by a handful of DMV-focused tools; the current count is an extreme mismatch between name and content.

Completeness1/5

The only DMV tool is ct_dmv_ev_registrations, covering EV registration counts from a single February 2025 snapshot. There is no general vehicle registration lookup, driver licensing, plate/ VIN search, appointment, or form coverage, so the DMV domain is severely incomplete.

Resources