chat_complete

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

With no annotations, the description carries the full burden, and it does disclose the key behavioral trait: inference runs through MCP sampling on the host agent's own model, with no API key. That is meaningful context an agent could not get elsewhere. It omits secondary traits such as sampling consent behavior or latency, keeping it below a 5.

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, front-loaded with the operation and immediately followed by the one fact that changes how an agent reasons about the call (the host model, no key). 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?

With a full output schema and 100% parameter coverage, return values and arguments need not be described, and the description supplies the mechanism context the structured fields lack. It could say more about sampling consent/prompting, but is largely sufficient for correct invocation.

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% and every parameter (model, messages, max_tokens, temperature) is documented in the schema, including the agent-default alias. The description adds only the redundant framing that the connected agent is the model, so it does not meaningfully extend the schema. 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 (Generate) and resource (chat completion) and pins down the request shape as OpenAI chat-completions-shaped. It also names the mechanism (MCP sampling via the host agent's model), but it never explicitly distinguishes itself from the siblings messages_create or list_models, so the agent must infer the boundary.

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 note that no API key is used implies the usage context (the connected agent is the model), but there is no explicit when-to-use or when-not-to-use guidance and no routing against the sibling messages_create. Usage is implied rather than stated.

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