chat

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

No annotations are provided, so the description must fully disclose behavior. It mentions a cost range of 10-1000 tokens but does not cover other important traits like statelessness, rate limits, error handling, or authentication requirements.

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 three sentences front-loaded with the core function. No filler: each sentence serves a purpose (main action, usage context, cost hint).

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 simple chat API tool, the description covers purpose, usage guidance, and cost. It lacks explicit response format details, but given no output schema, the default text response is reasonably inferred. Sibling tools are non-overlapping.

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% with descriptions for both parameters. The tool description adds context by specifying the default model and providing model ID examples, enhancing understanding beyond the schema alone.

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 verb 'Send a prompt' and the resource 'ANY chat model on avots.ai', listing multiple model families. It distinguishes from sibling tools by framing it as delegation to other models, e.g., 'have GPT-5.5 Pro double-check this reasoning'.

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 when to use: 'when you... want to delegate a sub-task to a different model' and gives concrete examples. However, it does not explicitly state when not to use or list alternative tools for similar tasks.

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