Dataset
datasetSingle dataset by id or slug.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id_or_slug | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
datasetSingle dataset by id or slug.
| Name | Required | Description | Default |
|---|---|---|---|
| id_or_slug | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false, so the safety profile is well covered. The description adds no additional behavioral context such as authentication requirements, error behavior, or rate limits, but it does not contradict the annotations.
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, front-loaded sentence with no unnecessary words. It is concise and easy to parse, though it could arguably include more useful detail without becoming verbose.
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?
Given the simple single-parameter tool and the presence of an output schema, the description is minimally viable. However, it lacks sibling differentiation and guidance on how to obtain the id/slug, making it slightly incomplete for an agent deciding between multiple related tools.
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 description coverage is 0%, so the description must compensate. It clarifies that the 'id_or_slug' parameter accepts either an ID or a slug, which adds some meaning beyond the bare parameter name. However, it does not provide format examples or clarify the difference between an ID and a slug.
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 'Single dataset by id or slug' clearly identifies the tool's purpose: retrieving one dataset using an identifier. The resource (dataset) and lookup method are explicit, though there is no explicit verb like 'get' or 'fetch'. It is distinguishable from search_datasets due to 'single', but this is not explicitly stated.
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?
There is no guidance on when to use this tool versus alternatives like search_datasets or entity_profile. The word 'single' implies a direct lookup, but no explicit context, exclusions, or recommended alternatives are provided.
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.