chat_completions

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

Annotations already note readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds substantial behavioral context: the default text.tokenizer is deterministic and non-generative, nothing is persisted, unsupported model ids fail with model_not_supported, and the tool lacks streaming and function-calling capabilities. This goes well beyond what annotations provide.

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 dense but front-loaded, with the core purpose in the first sentence, model routing and limitations in the second, and alternative tool guidance plus an error case in the third. Every sentence adds distinct value, and there is no repetition of schema or annotation details.

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 the small parameter surface, rich schema documentation, and no output schema, the description covers what an agent needs: the return shape, default model behavior, provider-backed model routing, non-features, persistence behavior, and the failure mode. Nothing critical is missing for selecting and invoking the tool correctly.

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 the baseline is 3. The description enriches the model parameter by explaining that text.tokenizer is a deterministic tokenizer-backed route returning a JSON tokenization summary, not a generative LLM. This directly informs parameter choice and output interpretation. The messages parameter is already well documented in 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?

The description states a specific verb ('Run'), a resource (one ordered chat transcript through an Algenta model), and the returned output (assistant message plus token usage). It also distinguishes this tool from sibling tools like responses and the tokenizer-backed default model.

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 explicitly says to use responses for independent single-string utility calls, explains that provider-backed chat models are routed through list_models, and clarifies what this tool does not do (no streaming, no function/tool calling). This gives an agent clear guidance on when to select this tool over alternatives.

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