Skip to main content
Glama

Data Toulouse

by io.github.pipeworx-io

Remember

remember
Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

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?

Annotations provide readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds: 'Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours' and 'scoped by your identifier', which enriches understanding beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is 4 sentences, front-loaded with the core purpose, then usage, then storage details, then partner tools. Every sentence earns its place; no fluff.

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 simple tool with 2 required params and no output schema, the description is complete: it covers purpose, usage context, persistence behavior, and related tools. No gaps remain.

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%, baseline is 3. The description adds practical examples for 'key' ('subject_property', 'target_ticker') and clarifies 'value' can be 'any text', adding meaning beyond the schema's descriptions.

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 'Save data the agent will need to reuse later' and provides concrete examples like 'a resolved ticker, a target address, a user preference'. It distinguishes from sibling tools 'recall' and 'forget' by mentioning them explicitly.

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 gives explicit guidance: 'Use when you discover something worth carrying forward... so you don't have to look it up again.' It also states to pair with 'recall' and 'forget', covering when to use and when not to.

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