datasetsSearch the Texas 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.
Input schema / examplesAdded value: +[
+ {
+ "query": "budget"
+ },
+ {
+ "limit": 10,
+ "offset": 0,
+ "query": "crime"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive behavior. The description adds value by specifying the catalog source and return format, which are not in annotations. No contradictions. For a read-only search tool, this is sufficient transparency.
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 sentences, front-loaded with action, no extraneous words. Every sentence serves a purpose: stating the function and summarizing output usage.
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 explains return fields and the next step (pass resource_id to query/metadata). It could mention pagination behavior, but offset is defined in the schema. For a search tool with strong annotations, this is fairly complete.
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% with clear parameter descriptions. The tool description confirms the query is a keyword search and mentions defaults, but does not add significant new meaning beyond the schema. 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 states the tool searches the Texas Open Data catalog by keyword, specifies returned fields (resource_id, name, description, category, update date), and explains how to use the result. This is a specific verb-resource pair that distinguishes it from siblings like 'query' or 'search_within'.
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 tells when to use the tool (keyword search for datasets) and hints at the next step (pass resource_id to query/metadata), but does not explicitly state when not to use it or compare to alternatives. However, the sibling list includes many unrelated tools, so the differentiation is implicit.
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.