datasetsSearch the Cook County Open Data catalog of open datasets by keyword. Returns each dataset's resource_id, name, description, category and update date — pass the resource_id to query/metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max datasets (1-100, default 20). | |
| query | No | Keyword to search dataset titles/descriptions (e.g. "budget", "crime", "health"). | |
| offset | No | Pagination offset. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "query": "crime"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "budget"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering safety. The description adds value by specifying the exact output fields and the downstream use of resource_id, which goes beyond annotation information and helps the agent understand the tool's results.
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 concise sentences that front-load the action and scope, then list return fields and next steps. Every word earns its place, and there is no redundancy or filler.
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?
For a simple search tool with rich annotations, full schema coverage, and no output schema, the description provides essential return fields and usage guidance. It tells the agent what the tool returns and how to proceed, making it complete and self-contained.
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 all three parameters (query, limit, offset), so the baseline of 3 applies. The description does not add extra parameter-level detail beyond the schema, only mentioning 'by keyword' which aligns with the query parameter.
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's function: 'Search the Cook County Open Data catalog of open datasets by keyword.' It names a specific resource and verb, and lists return fields, distinguishing it from sibling tools like query/metadata. The addition of 'pass the resource_id to query/metadata' further clarifies its role in the workflow.
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 when to use the tool (when searching by keyword) and explicitly instructs the next step ('pass the resource_id to query/metadata'), offering clear usage context. However, it does not explicitly state when not to use it or name alternative search tools, leaving a slight gap.
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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