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Glama

Oregon 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 read-only, idempotent, and non-destructive behavior. The description adds detail about fan-out, fallback logic (GDELT→GNews), soft-fail for USPTO, and output structure (changes grouped by source with citations). No contradiction with annotations.

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 a single paragraph that packs information efficiently, but could be slightly more structured (e.g., bullet points) for easier scanning. Every sentence contributes value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-source, fallback, soft-fail) and lack of output schema, the description adequately covers behavior and output format. Missing details on pagination or limits are minor gaps.

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 description adds value by providing examples for `since` (ISO date or relative shorthand), acceptable values for `value` (ticker or CIK), and clarifying `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 clearly states it provides a change feed for a company, using multiple sources like SEC EDGAR, GDELT/GNews, and USPTO. It distinguishes itself from the sibling tool entity_profile by specifying when to use each.

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 includes explicit usage examples (e.g., 'What's new with X') and contrasts with entity_profile: 'Use entity_profile instead when you want the static profile... regardless of window.'

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

All Oregon DMV toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several tools occupy the same functional space: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and the Polymarket cluster (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, bet_research) heavily overlaps in purpose. The only clearly separated tools are the two DMV-specific ones, but they are drowned out by ambiguous data-query and prediction-market tools.

Naming Consistency4/5

Tool names mostly follow a predictable snake_case verb_noun pattern such as list_subscriptions, resolve_entity, validate_claim, and the polymarket_* / or_dmv_* prefixes are consistent. Minor deviations like bet_research, pipeworx_feedback, and ask_pipeworx_beta break the pattern slightly, but the overall style is coherent and readable.

Tool Count1/5

A server named 'Oregon DMV' exposes 33 tools, only 2 of which relate to DMV office locations and wait times. The other 31 tools form a broad general-purpose data and prediction-market platform, making the count and scope an extreme mismatch for the stated server identity.

Completeness1/5

For an Oregon DMV server, the surface is severely incomplete: there are no tools for appointments, forms, fees, licensing, registration, or services. The two DMV tools cover only office addresses and live wait times, covering a tiny slice of the domain implied by the server name.

Resources