search_datasetsSearch Issy-les-Moulineaux Open Data for datasets by keyword (city services, mobility, environment & smart-city data). Returns dataset_ids (pass to query/dataset_info), titles, themes and record counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword(s) to search dataset titles/descriptions. | |
| offset | No | Pagination offset (default 0). |
Changes observed during successful MCP inspections.
Input schema / examplesAdded value: +[
+ {
+ "query": "mobility transport"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "environment air quality"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true. The description adds value by specifying return fields (dataset_ids, titles, themes, record counts) and how to use IDs with other tools. 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?
Two concise sentences with no fluff. First sentence states purpose and scope, second sentence details return values and usage. Every sentence is valuable, and the description is front-loaded with key information.
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 tool's complexity (3 parameters, no output schema, good annotations), the description covers purpose, return values, and integration with other tools. It could mention sorting or data source limits, but is sufficient for an agent to 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?
Schema covers 100% parameters with clear descriptions. The description enhances meaning by providing example keyword categories (city services, mobility, environment) and indicating the tool is limited to Issy-les-Moulineaux data. This adds domain-specific context beyond the schema.
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 searches Issy-les-Moulineaux Open Data datasets by keyword, with specific categories (city services, mobility, environment & smart-city data). It distinguishes itself from sibling tools like query, dataset_info, and search_within by describing what it returns and how to use the results.
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 implies usage by mentioning that dataset_ids can be passed to query/dataset_info, providing integration context. It gives example keywords, but does not explicitly state when not to use this tool or compare to alternatives like search_within. Still, it offers clear guidance for typical use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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