flow_validate

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

Annotations already flag openWorldHint and destructiveHint, so the description need not repeat them. The description adds valuable context: the tool runs asynchronously, returns a job_id immediately, and takes ~3-5 minutes. This goes beyond the structured annotations, though it does not elaborate on the destructive hint.

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?

Three sentences, front-loaded with the primary purpose, then essential workflow details. No redundant or filler content; every sentence earns its place.

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?

The description covers the core purpose, the async behavior (including time estimate), the prerequisite, and the monitoring mechanism. An output schema exists, so return values need not be described. This is complete for a moderately complex tool.

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%; all three parameters have descriptions in the schema. The tool description does not add additional parameter meaning beyond the schema, so baseline 3 is appropriate.

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 'Validate a trained Flow model against real data' and lists specific evaluation metrics (Wasserstein distance, KS tests, coverage tests, marginal distribution checks). This distinguishes it from sibling tools like train_flow_model and generate_flow_paths.

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?

Explicitly provides a prerequisite ('Requires a trained Flow model (run train_flow_model first)') and names the follow-up tool to monitor progress ('Use check_flow_job(job_id=..., job_type='validate')'). This gives clear when-to-use guidance.

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