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

Annotations only declare it is a non-read-only, non-destructive, closed-world write. The description adds genuinely non-obvious behavioral context: parameter validity is model-dependent (Opus 5 accepts reasoningEffort and rejects temperature/topP), which prevents malformed calls. It does not cover permissions, whether creation is reversible, or rate limits.

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 tight sentences with the critical constraint front-loaded as 'IMPORTANT' and the workflow caveat marked 'NOTE'. No filler. The trailing create_run sentence is slightly tangential to the act of creation, but it earns its place as sequencing guidance.

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 write tool with no output schema, the description covers the highest-risk decision (model selection and its parameter consequences) and the immediate next step in the workflow. It does not mention what the call returns (e.g., an agent ID needed for create_run), which is the main remaining 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 description coverage is 100%, so the baseline is 3, but the description adds cross-parameter semantics the schema cannot express: that llmModel gates which sampling/reasoning parameters are legal. That is meaningful beyond the per-field descriptions, though the remaining 15 parameters are left entirely to 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?

States a specific verb+resource ('Create a new agent') and adds the platform context ('for optimization'), which distinguishes it from sibling read/mutation tools like get_agent, list_agents, and update_agent. Sibling differentiation is implied by the verb rather than stated explicitly, so it falls just short of a 5.

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 concrete when-to-use guidance: llmModel must be specified, the agent should ask the user which model is in use when unspecified, and it names the follow-up step and sibling tool ('upload run transcripts with create_run before running optimization'). No when-not-to-use or prerequisite/permission guidance is offered, keeping it below 5.

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