Superset MCP Integration

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It mentions the API endpoint '/api/v1/dataset/{id}' which adds implementation context, and states it 'retrieves detailed information including columns and metrics', giving some behavioral insight. However, it doesn't disclose important traits like whether this is a read-only operation (implied but not stated), authentication requirements, error handling, or rate limits. For a tool with no annotations, this leaves significant gaps.

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 appropriately sized with three clear sections: purpose statement, implementation detail, and parameter/return documentation. It's front-loaded with the core purpose. The Args/Returns structure is helpful, though slightly verbose for a single parameter. Every sentence adds value without 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?

Given 1 parameter with 0% schema coverage and no output schema, the description provides basic purpose and parameter semantics but lacks comprehensive context. It doesn't explain the return structure beyond 'dictionary with complete dataset information', doesn't mention error cases, and with no annotations, leaves behavioral aspects underspecified. For a simple read operation, it's minimally adequate but incomplete.

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 description adds the parameter 'dataset_id' with explanation 'ID of the dataset to retrieve', which provides semantic meaning beyond the schema's basic 'integer' type. However, with 0% schema description coverage and only 1 parameter, this minimal addition meets the baseline. It doesn't elaborate on ID format, sourcing, or validation, leaving room for improvement.

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 purpose: 'Get details for a specific dataset' with the specific resource 'dataset' and verb 'get details'. It distinguishes from siblings like 'superset_dataset_list' (which lists datasets) and 'superset_dataset_create' (which creates datasets). However, it doesn't explicitly mention what 'details' include beyond 'columns and metrics' in the second sentence.

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 context through 'specific dataset' and the parameter 'dataset_id', suggesting this is for retrieving details of a known dataset. However, it doesn't explicitly state when to use this versus alternatives like 'superset_dataset_list' for browsing datasets or other get_by_id tools for different resources. No explicit when-not-to-use guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.