Skip to main content
Glama

Datagouv Fr

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)

TDQS

A4.5/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds context beyond annotations: key-value pair scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. Doesn't contradict annotations, but doesn't specify overwrite behavior for existing keys.

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?

Every sentence adds value — purpose, use cases, storage details, pairing with related tools. No fluff or repetition.

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?

Fully describes behavior for a simple 2-param save tool, including persistence semantics, scoping, and relationships to recall/forget. No output schema needed for a save operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema already covers both parameters with examples. Description restates the type of content to store (ticker, address, preference) but adds no new syntax or format details beyond the 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?

States 'Save data the agent will need to reuse later' with a specific verb and resource. Clearly distinguishes from siblings recall (retrieve) and forget (delete).

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 when to use ('when you discover something worth carrying forward') and names alternatives: 'Pair with recall to retrieve later, forget to delete.'

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

All Datagouv Fr 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

B3.4/5.0
Disambiguation2/5

Multiple tools are near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share nearly identical behavior, and validate_claim overlaps heavily with ask_pipeworx_grounded. ai_visibility_check vs scan_competitor_ai_presence also overlap. dataset/organization vs search_datasets/search_organizations could confuse agents.

Naming Consistency2/5

Naming is inconsistent: snake_case (ask_pipeworx, deep_research, entity_profile), long descriptive names (scan_competitor_ai_presence, polymarket_edge_tracker), and terse single words (dataset, organization). No coherent naming convention across the set.

Tool Count2/5

37 tools is heavy, and most are Pipeworx platform tools (Polymarket analysis, npm dependency scanning, llms.txt generation, memory ops) that are far out of scope for a 'Datagouv Fr' French open-data server. Only ~6 tools (search_datasets, search_organizations, reuses_search, resources, dataset, organization) relate to the server's stated domain.

Completeness3/5

The data.gouv.fr browsing surface is reasonably complete: search datasets, search organizations, list reuses, fetch resources, get dataset/organization by slug. However the server lacks common catalogue operations like downloading a resource from a URL, inspecting dataset metadata details, or community/follow features — plus the bulk of tools target entirely different domains, leaving the actual data.gouv.fr scope thin.

Resources