analyze_dataset

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

Annotations already declare readOnlyHint=true and destructiveHint=false, indicating safe read-only behavior. The description adds value by detailing the return types (dataset URLs, lens structures, etc.) but does not cover potential behavioral aspects like rate limits or data size. Overall, it provides adequate context beyond annotations.

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 with no wasted words: first sentence states the action, second sentence lists the return contents. The critical guidance about finding slugs is embedded in the parameter description, keeping the main description concise.

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 the tool has only one parameter with complete schema documentation, no output schema, and annotations present, the description fully covers what the tool does, what it returns, and how to obtain valid input. No gaps remain.

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 100% for the single parameter 'slug'. The description adds the instruction 'Use read_investigations to list available slugs', which enriches the schema's example-based description with a practical source for valid values.

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 verb 'fetch' and the resource 'raw CSV datasets and investigation metadata', and lists specific return items (dataset URLs, lens structures, correlations, questions, outliers). It distinguishes from the sibling 'read_investigations' which lists available slugs.

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 parameter description for 'slug' advises using 'read_investigations' to list available slugs, providing clear guidance on prerequisite usage. However, it does not explicitly state when not to use this tool or compare to other sibling tools beyond that hint.

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