search_datasetsSearch France Economy & Finance Open Data for datasets by keyword (public finance, companies, taxation, procurement & economic indicators). 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. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "public finance"
+ },
+ {
+ "limit": 10,
+ "query": "taxation companies"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds domain specificity (France Economy & Finance) and return fields, which supplements the annotations without contradicting them. It provides useful context beyond the safety profile.
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 two sentences, front-loading the core purpose and immediately following with return values and usage guidance. Every word adds value 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?
Despite no output schema, the description fully explains return values (dataset_ids, titles, themes, record counts) and suggests next steps. Parameters are fully documented in schema, and the domain is clearly specified. Complete for a search tool.
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 coverage is 100%, so all parameters are described in the schema. The description does not add extra meaning beyond the schema; it only restates that search is by keyword. Baseline 3 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 specifies the verb 'Search' and the resource 'France Economy & Finance Open Data for datasets', with specific keyword domains. It also states the return values (dataset_ids, titles, themes, record counts) and explicitly mentions passing dataset_ids to sibling tools (query/dataset_info), distinguishing it from those tools.
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 a workflow by stating 'Returns dataset_ids (pass to query/dataset_info)', which guides use as an initial search step. However, it does not explicitly mention when not to use this tool or provide alternatives among siblings, so it is clear but lacks exclusions.
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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