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Glama

Maryland MVA

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description details fan-out behavior, fallback logic, soft-fail for USPTO, relative date parsing, and return format.

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 dense but well-structured, starting with query examples, then parallel call, parameters, return format, and sibling note. Every sentence adds value, though slightly long.

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 tool's complexity (multiple sources, fallbacks, no output schema), the description is remarkably complete, covering behavior, parameters, and return structure.

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?

Adds meaning beyond schema: explains 'since' accepts ISO or relative shorthand with examples, and 'value' can be ticker or CIK. Schema coverage is 100%.

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 explicitly states it's a change feed for a company, lists sources (SEC EDGAR, GDELT→GNews, USPTO), and distinguishes from entity_profile. It provides clear query examples.

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 says when to use the tool (for 'what's new' queries) and explicitly recommends 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 Maryland MVA toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools are distinct, but there are several problematic clusters: ask_pipeworx, ask_pipeworx_beta (which admits it is currently identical to the stable router), and ask_pipeworx_grounded can easily be misselected. The Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have fuzzy boundaries around 'find me an edge', and compare_entities/entity_profile/recent_changes all offer one-call company research.

Naming Consistency4/5

Naming is overwhelmingly lowercase snake_case with a verb-first pattern (ask_, generate_, list_, resolve_, subscribe, recall). Deviations include the md_dmv_ prefix on two tools, the noun-led entity_profile, and bare verbs like forget/remember/recall, but the overall style remains readable and predictable.

Tool Count2/5

33 tools is well into the bloated range for a single server, and the sprawl is worsened by the fact that only 2 of the 33 tools actually relate to the server's stated name, 'Maryland MVA.' The rest form an unrelated Pipeworx data/prediction-market/memory/meta-toolkit that could either be split into separate servers or consolidated behind the universal router.

Completeness3/5

For the actual Pipeworx domain, coverage is broad: universal routing, grounded answering, deep research, entity profiles, comparisons, claim validation, memory, and subscriptions are all present. However, for the server's apparent purpose — Maryland MVA — coverage is nearly empty: only vehicle registration counts and EV adoption exist, with no driver services, fees, offices, titles, or licensing operations. The direct tool surface also has holes (stock prices, weather, etc.) that only the meta-router papers over.

Resources