list_datasetsList public ClinicMarketIQ dataset families from the canonical catalog. Returns metadata summaries, not observation rows.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| offset | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this read-only, idempotent, non-destructive, and open-world; the description adds that results are metadata summaries rather than observation rows, a useful behavioral clarification.
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 tightly worded sentences, front-loaded with the core action and a single clarifying detail; no 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 paginated list tool, the description plus annotations cover safety and output type; missing only explicit pagination guidance and sibling routing, which are minor.
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 description coverage is 0% and the description says nothing about limit/offset semantics, defaults, or pagination, so it does not compensate for the missing parameter documentation.
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 names the exact resource ('public ClinicMarketIQ dataset families from the canonical catalog') and clarifies the output ('metadata summaries, not observation rows'), which separates it from row-returning 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 context of 'canonical catalog' implies use for browsing available public datasets, but there is no explicit when-to-use or contrast with siblings like search_datasets or get_dataset_metadata.
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.
Each tool targets a clearly distinct operation: catalog discovery (list/search), metadata retrieval, download URL retrieval, schema retrieval, and checksum verification. The only potential overlap is between metadata and download URL, but the descriptions make their boundaries explicit.
Tool names consistently follow a snake_case verb_noun pattern: get_*, list_datasets, search_datasets, verify_checksum. The naming style is uniform and every verb accurately reflects the action.
Six tools is a well-scoped set for a public dataset metadata and access server. Each tool covers a necessary step in the discovery-to-download workflow without redundancy.
The workflow is complete for the stated domain: discover datasets, inspect metadata and schema, obtain download URLs, and verify checksums. Direct dataset bytes are intentionally not proxied, so this is not a gap given the server's defined purpose.