generate_seed_data

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

Without annotations, the description bears full burden for behavioral disclosure. It effectively communicates key behaviors: generating realistic data, respecting constraints, and supporting multiple output formats. It also notes Pro features (seed, CSV). However, it does not discuss error handling or limitations.

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 highly concise, comprising three focused sentences. It front-loads the primary purpose, then explains features, and finally specifies input/output options. No wasted words.

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 tool with 4 parameters (100% schema coverage) and no output schema, the description provides sufficient context: input types, output formats, and core behavioral guarantees. It lacks mention of error conditions but is otherwise complete for its intended use case.

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%, providing detailed parameter descriptions. The tool description adds value by noting defaults (rows=10), Pro features (seed, CSV), and the schema input format. This extra context elevates the score above baseline 3.

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: generating realistic, referentially-coherent test data. It specifies key features (foreign key resolution, unique constraints, realistic values) and distinguishes itself from the sibling tool 'analyze_schema' by focusing on data generation.

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 explains when to use the tool (generating test data from a schema) and mentions input options (SQL DDL or JSON schema) and output formats. However, it does not explicitly state when not to use it or provide direct comparison with the sibling tool, leaving some ambiguity.

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