resourcesList all downloadable resources (CSV, JSON, XLS, API, etc.) attached to a data.gouv.fr dataset, identified by id or slug; returns file URLs, formats, and last-update dates.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id_or_slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'List all downloadable resources', implying an exhaustive result set, while the annotations include openWorldHint: true, which explicitly tells the agent that the results may not be a closed world and should not be assumed complete. This is a direct contradiction, potentially misleading the agent about the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the core purpose ('List all downloadable resources'), then adds essential details about resource types, input, and output. Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main purpose, input, and output types, and an output schema exists, so full return structure is not needed. However, the 'List all' claim directly conflicts with the openWorldHint annotation, which is a critical missing caveat about result completeness. This makes the description misleading and incomplete for correct agent behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no description for the parameter dataset_id_or_slug (coverage 0%), but the description compensates fully by stating it can be an id or slug and gives an example. This clearly defines the parameter's meaning and format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb ('List') and resource ('downloadable resources'), specifies the input (dataset id or slug) and output (file URLs, formats, last-update dates). This distinguishes it from sibling tools like 'dataset' which likely returns metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description unambiguously indicates when to use it: when you need the list of downloadable resources attached to a data.gouv.fr dataset. It provides the input requirement (id or slug), making the usage context clear. However, it does not explicitly mention alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.