train_flow_model

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

The description discloses key behavioral traits beyond annotations: it returns immediately, training runs on a GPU and takes 5-15 minutes, and the agent should not auto-poll. These are important operational details not captured by openWorldHint or destructiveHint. 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?

The description is three sentences, front-loaded with the core purpose. The second sentence is dense with operational instructions, but every part is useful for safe usage. It could be slightly more concise, but avoids fluff and stays on topic.

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 async lifecycle, follow-up mechanism, and prerequisites, which is rich for a tool with an output schema. It doesn't explain return values, but that is covered by the output schema. The main missing piece is guidance on handling training failure, but this is a minor gap.

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 input schema already provides detailed descriptions for all 5 parameters (100% coverage), including the 'tickers or portfolio_id' alternative and the source of conditioning_set_id. The description's mention of 'tickers or portfolio_id' is redundant and adds no new meaning beyond the schema, so the baseline of 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 states the tool trains a generative Flow model on a portfolio and conditioning set, using a specific verb and resource. It distinguishes itself from siblings by clarifying that it returns immediately and that progress must be checked via check_flow_job, which is not part of this tool.

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 provides explicit when-to-use guidance: after calling, stop and tell the user training started; let them keep chatting; when they ask about progress, use check_flow_job. It also explicitly states do NOT automatically poll or call check_flow_job yourself, which is a clear exclusion. Prerequisites for required parameters are given.

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