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Glama

New York State 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

A5/5.0
Behavior5/5

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

The description details multi-source fan-out (SEC, GDELT/GNews, USPTO), fallback behavior, and return structure. Annotations already cover safety, but the description adds valuable behavioral context beyond them.

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 and is structured logically. Every sentence adds value with no redundant information.

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?

Given the complexity of multiple sources, fallback, and no output schema, the description covers return structure, parameter details, and distinguishes from siblings, making it fully complete.

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%, and the description adds meaning beyond the schema by explaining accepted formats for 'since', clarifying 'value' as ticker or CIK, and confirming 'type' only supports 'company'.

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 uses specific verbs and resources: 'change feed for a company' and provides example queries. It clearly distinguishes from sibling tool entity_profile by stating when to use which.

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 examples and directly tells the agent to use entity_profile for static profiles, providing a clear alternative.

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

All New York State DMV toolsInspect
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TDQS

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all answer questions via the same router). The set also includes generic memory and subscription tools that are unrelated to the DMV domain, making it hard for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

No consistent naming pattern across the set. Some tools use a ny_dmv_ prefix, while others use generic verbs like ask_pipeworx, deep_research, or single-word names like recall, subscribe. This mix of conventions and styles makes the naming chaotic.

Tool Count2/5

At 37 tools, the count is too high for a server purportedly about the NY DMV. Only about 7 tools are DMV-specific; the rest are generic Pipeworx utilities that inflate the surface unnecessarily. A focused DMV server should have fewer, more targeted tools.

Completeness3/5

The DMV-specific tools cover key areas: driving schools, facilities, offices, road test sites, registrations, and EV adoption. However, common DMV operations like license renewal, appointment scheduling, or fee lookups are missing. The generic tools add breadth but not depth for the DMV domain.

Resources