Chat

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

With no annotations, the description carries the full burden. It discloses the required message format, the start_server prerequisite, and the return shape, but says nothing about streaming vs blocking behavior, error/permission conditions, or how max_tokens/temperature affect generation.

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?

Two tight sentences, purpose front-loaded, no filler. The trailing list of return fields is slightly redundant given an output schema exists, but it is brief enough not to bloat the definition.

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?

An output schema exists so return values need not be described, and the description usefully names the prerequisite. However, for a generative inference call with no annotations, the omission of operational traits (blocking behavior, failure modes, meaning of the sampling params) leaves it only minimally complete.

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 0%, so the description must compensate. It does explain the required messages parameter's structure ([{"role","content"}]), but max_tokens and temperature are undocumented in both schema and description, leaving two of three parameters semantically opaque.

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 a specific verb and resource: send a conversation to the already-loaded local model, with the OpenAI message shape given inline. It is clearly the inference/chat entry point versus siblings like list_models or server_status, though it does not explicitly contrast itself with them.

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?

"先用 start_server 启动模型" states an explicit precondition and names the sibling tool that satisfies it, which is real routing guidance. It does not describe when to avoid this tool or how it relates to stop_server / server_status, so it falls short of full when/when-not coverage.

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