Skip to main content
Glama

Data Toulouse

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?

Describes parallel fetching, fallback from GDELT to GNews, USPTO soft-fail, and return structure. Consistent with annotations (readOnlyHint, idempotentHint, destructiveHint=false). No contradictions.

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?

Information-dense but well-structured: starts with examples, explains behavior, then alternative tool. Could be slightly more concise but no wasted sentences.

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?

For a tool with 3 params and no output schema, the description fully explains input formats, sources, fallback, limitations, and return structure. Complete for effective use.

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 adds usage hints like recommended 'since' format and example values. Adds meaning beyond 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?

The description clearly states the tool is a change feed for a company over a time window, listing specific sources (SEC, GDELT/GNews, USPTO) and distinguishing it from entity_profile. Example queries clarify the intent.

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?

Explicitly says to use entity_profile for static profiles, implying this tool is for recent changes. Provides guidance on when to use each alternative.

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

All Data Toulouse toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially the multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread). Users will struggle to choose the right tool without deep reading.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, bet_research), others camelCase (generate_llms_txt, list_subscriptions), and many are long phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). No consistent pattern.

Tool Count3/5

34 tools is high but not extreme. The server covers diverse domains (company data, drugs, economics, prediction markets, open data, memory utilities), but many tools are very specific and could be consolidated, making the set feel bloated.

Completeness3/5

The server covers many data sources but has notable gaps: simple market listing tools are missing for Polymarket, and the Toulouse Open Data tools are limited to querying only (no create/update/delete). Memory and subscription tools seem ancillary to the core data mission.

Resources