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Glama

California 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

A4.6/5.0
Behavior5/5

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

Annotations already mark the tool as readOnlyHint, openWorldHint, idempotentHint, destructiveHint false. The description adds critical behavioral context: fan-out to SEC EDGAR, GDELT→GNews fallback with rate-limit handling, USPTO soft-fail due to API sunset, and date parsing (ISO vs relative). None of this is in the annotations, adding significant value.

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 dense but effective, using examples to illustrate intent and concisely summarizing behavior. It could be slightly more structured (e.g., separate source behaviors), but every sentence adds value without redundancy.

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 no output schema, the description still specifies the return format (structured changes by source, total_changes, citation URIs). It covers all parameters, data source behaviors, and use-cases. For a multi-source tool with fallbacks, this is complete and actionable.

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 coverage is 100%, so baseline is 3. The description adds meaning by clarifying that 'type' only supports 'company', providing examples for 'since' (ISO and relative) and a recommended default '30d', and explaining 'value' as ticker or zero-padded CIK. This improves usability beyond the 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 starts with concrete example queries, identifies the tool as a change feed for a company within a time window, lists three data sources, and explicitly distinguishes from the sibling 'entity_profile' tool in the last sentence. This clearly states what the tool does and differentiates it.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides when-to-use context through example queries and mentions the alternative 'entity_profile'. However, it does not explicitly state when not to use this tool or compare with other siblings like 'ask_pipeworx' or 'deep_research'. Still, the guidance is clear enough for typical use cases.

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

The DMV-specific tools are individually distinct, but they are buried among ~30 unrelated Pipeworx utilities with several overlapping pairs (ask_pipeworx variants, entity_profile/compare_entities/recent_changes, and the polymarket suite). An agent pointed at this server cannot reliably tell which tools belong to the California DMV domain versus the general data platform.

Naming Consistency3/5

Most tools use lowercase snake_case, and the ca_dmv_* family is consistent, but there is no coherent semantic pattern across the set: some names are noun phrases (entity_profile), some verb phrases (discover_tools, validate_claim), and many are product-specific prefixes (ask_pipeworx, polymarket_*). The formatting is consistent, but the naming conventions are mixed.

Tool Count1/5

37 tools is far too many for a California DMV server; only 6 are actually DMV-specific. The remaining ~30 tools cover general Pipeworx data lookup, prediction markets, memory, subscriptions, and AI visibility, which belong in a separate server entirely.

Completeness2/5

For the stated DMV scope, the surface is thin: it covers registrations, licenses, offices, forms, insurance codes, and EV adoption, but misses common DMV queries like registration fees, title/status lookups, and appointment or transaction data. The 30 unrelated tools do not fill these domain gaps and instead obscure them.

Resources