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

Annotations are minimal (readOnlyHint=false, openWorldHint=true), so the description carries some burden. It adds valuable behavioral context: the free preview limit for table schemas, the requirement of an API key for prompt/larger requests, and the 'proof-of-work' nature of the free tier. These details go beyond what annotations indicate and help manage expectations. No contradiction with 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, front-loaded with the core purpose. Every phrase earns its place: formats, input methods, free-tier constraints, and API key requirements. No fluff or repetition. Exemplary efficiency.

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?

With an output schema available, the description needn't detail return values. It covers the two input modes, output formats, key usage constraints, and the API key distinction. It does not explain sql_dialect or seed, but those are optional and schema-documented. The free preview vs production boundary is a critical piece of context that is included. Overall, it is sufficiently complete for effective tool invocation.

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 meaning to the main parameters by stating data sources ('structured tables', 'plain-English prompt') and output formats ('JSON, CSV, or SQL'). Schema coverage is 57%, so it partially compensates for undocumented parameters. The 'up to 100 records' hint clarifies the 'count' field context, and the API key requirement clarifies the api_key parameter. This adds useful semantic context beyond the schema.

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 starts with a clear verb and resource: 'Generate realistic JSON, CSV, or SQL test data' and specifies two input methods ('structured tables or a plain-English prompt'). This precisely distinguishes it from sibling tools like generate_from_template or detect_schema. The purpose is unambiguous and action-oriented.

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?

Provides explicit context for when the tool can be used without an API key ('Explicit table schemas up to 100 records can run free') and when a key is required ('plain-English prompt generation, larger requests, and production usage'). This is practical usage guidance, though it does not mention alternative tools or exclusion criteria. The guidance is clear enough for selecting the tool in different scenarios.

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