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Glama

Data Detroit

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?

Discloses multi-source fan-out, fallback logic, soft-fail for USPTO, and return structure (changes[], total_changes, citation URIs). Annotations already indicate read-only, idempotent, non-destructive, and open-world; description adds substantial behavioral context without contradiction.

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?

Single paragraph that front-loads query examples and then details behavior. Every sentence contributes useful information; no redundancy. Could benefit from slight structuring (e.g., bullet points) but remains efficient.

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?

Despite no output schema, the description fully explains return values and behavior. It covers multi-source behavior, fallback, soft-fail, time window formats, and cross-reference to sibling tool. Given tool complexity, this is complete.

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% but description still adds value by explaining accepted date formats ('2026-04-01', '7d', '30d'), recommending typical values ('30d' or '1m'), and providing examples for value (ticker or CIK). This exceeds baseline 3.

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 the tool provides a change feed for a company, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly distinguishes from sibling tool entity_profile for static profiles.

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?

Includes explicit guidance on when to use this tool vs. alternatives — 'Use entity_profile instead when you want the static profile...' — and provides concrete example queries. Also explains fallback behavior between GDELT and GNews.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, creating direct ambiguity. The five polymarket tools and the ask/research/entity family (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes) also blur boundaries, making selection error-prone.

Naming Consistency3/5

Most names are snake_case with useful domain prefixes (detroit_*, polymarket_*, ask_pipeworx_*), but verb styles vary widely (ask, generate, scan, validate, suggest, remember) and some names like recent_changes vs recent_alerts are confusable. No strict pattern unifies the set.

Tool Count2/5

At 34 tools the server is overloaded; alongside the core universal-data and Detroit-query tools it also carries memory, subscriptions, feedback, AI-visibility, and npm-scanning tools that feel outside the stated scope. Many of these could be split into separate servers or trimmed.

Completeness3/5

The query surface is broad and the memory/subscription sub-domains have full lifecycle coverage, but the 'Data Detroit' identity is thin (only three city-specific tools) and the catalog-heavy design relies heavily on meta-tools rather than direct data operations. Some gaps remain, like a straightforward way to fetch a raw record by citation.

Resources