dataset_infoGet metadata for one Rennes Métropole Open Data dataset (fields/schema, themes, record count) — call before query to learn the column names.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset_id | Yes | Dataset id from search_datasets. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "dataset_id": "bike-stations-rennes"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds specific behavioral context by listing return types (fields/schema, themes, record count). No contradictions with 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 zero wasted words. It efficiently communicates purpose and usage context.
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 no output schema, the description lists the specific metadata returned (fields/schema, themes, record count). For a simple, single-parameter tool, this is complete and sufficient for an AI agent to invoke and use correctly.
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 input schema has 100% description coverage for the single required parameter 'dataset_id' with a description referencing search_datasets. The tool description does not add additional parameter meaning beyond what the schema already provides, so baseline score is appropriate.
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 the tool retrieves metadata for one Rennes Métropole Open Data dataset, listing specific information (fields/schema, themes, record count). It also provides a usage hint to call before querying to learn column names, distinguishing it from search_datasets.
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 explicitly advises calling this tool before a query to learn column names, indicating clear usage context. However, it does not explicitly state when not to use it or mention alternatives beyond the implicit sibling tool search_datasets.
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.
Many tools have overlapping purposes, such as multiple data query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and several prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). While descriptions attempt to differentiate, an agent could easily select the wrong tool.
Tool names use a mix of verb-initial (ask_pipeworx, compare_entities) and noun-phrase patterns (entity_profile, dataset_info), with no consistent verb_noun structure. Naming is readable but lacks a predictable pattern.
With 34 tools, the server is on the heavy side. The broad scope (data querying, prediction markets, company research, local open data) somewhat justifies the count, but several tools could be consolidated (e.g., the various polymarket tools).
The tool set covers a wide range of functionalities including data querying, company research, prediction market analysis, and memory management. Minor gaps exist (e.g., no user authentication tools beyond subscriptions), but the surface is largely comprehensive for its intended purpose.