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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 six ca_dmv_* tools are clearly distinct, but the set also contains near-duplicates like ask_pipeworx and ask_pipeworx_beta (currently functionally identical), five overlapping Polymarket analysis tools, and two overlapping AI-visibility probes. An agent would frequently struggle to select the right tool among these overlapping families.

Naming Consistency3/5

Names are mostly lowercase snake_case and individually readable, but conventions are mixed: ca_dmv_* prefix vs. bare verbs like forget, compound names like generate_llms_txt, and near-identical pairs like polymarket_edges vs. polymarket_edge_tracker. The pattern is inconsistent enough to impede quick scanning.

Tool Count1/5

37 tools for a server named 'California DMV' is an extreme scope mismatch: only 6 tools relate to DMV while 31 are general-purpose research, prediction-market, memory, and subscription tools. The DMV-specific subset would be well-scoped at 6 tools, but the bundled platform makes the server feel bloated and off-topic.

Completeness2/5

The DMV portion covers licenses, registrations, EV adoption, offices, forms, and insurance codes, but misses common DMV needs like title transfers, fee estimates, or appointment booking. The broader research platform is extensive, but that does not compensate for the server's stated purpose, leaving the DMV surface with significant gaps.

Resources