perf_chat

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

With no annotations provided, the description carries the full responsibility. It discloses automatic model selection, retries, fallbacks, streaming, cost savings, and OpenAI-compatible format. It does not detail specific error handling or rate limits, but overall provides good behavioral context.

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 concise at three sentences, front-loaded with the main purpose, and every sentence provides necessary information without redundancy.

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?

Given no output schema, the description mentions 'OpenAI-compatible format', which implies a standard response structure. It covers key behaviors like streaming and cost savings. Could explicitly state the return format, but is sufficient for most agents.

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% with all 5 parameters described. The description adds value by explaining automatic model selection, retries, fallbacks, and cost savings, which are not captured in the schema alone. It complements the parameter descriptions effectively.

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 the tool routes LLM requests to the optimal model automatically, selecting from 20+ models based on task complexity and cost. It distinguishes from direct API calls to OpenAI/Anthropic and from sibling tools (perf_correct, perf_validate, perf_verify).

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 advises using this tool instead of calling OpenAI or Anthropic directly, indicating a clear replacement use case. It does not explicitly state when not to use or compare with siblings, but the context is clear for an automated routing tool.

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