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

Annotations only tell the agent this is a non-read-only, non-destructive write. The description adds substantive behavior beyond that: model-dependent parameter compatibility (Opus 5 uses reasoningEffort and rejects temperature/topP) and the post-creation prerequisite of create_run. It omits whether creation is reversible or what the response contains.

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?

Purpose is front-loaded in the first clause, followed by the high-value model caveat and the workflow note. The capitalized IMPORTANT/NOTE markers are slightly heavy-handed, but each sentence carries real information with no padding.

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 16-parameter creation tool with no output schema, the description covers the two things an agent most needs: the required model choice and the follow-up create_run step. It does not say what the creation returns (e.g. an agent id needed downstream), which is the main residual 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?

Schema coverage is 100%, so every parameter including llmModel, temperature, topP and reasoningEffort is already documented inline. The description reinforces the model-to-parameter interaction, but that information is duplicated from the schema rather than adding new semantics, so the baseline 3 applies.

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?

States a specific verb and resource ('Create a new agent for optimization'), making it distinguishable from update_agent, delete_agent, list_agents and the adjacent create_run/create_persona siblings. It does not explicitly name an alternative, but the purpose is unambiguous.

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?

Gives clear procedural guidance: specify the LLM model, ask the user which model when unspecified, and upload run transcripts with create_run before running optimization. That is real when-to-use sequencing, though it never contrasts with update_agent for modifying an existing agent.

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