send_message

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

No annotations are provided, so the description must fully disclose behavioral traits. It only mentions sending a message and getting a response, without covering side effects, required permissions, error behavior, or any mutational implications. This is insufficient for a tool that likely writes to a conversation.

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 a single, direct sentence that efficiently states the tool's purpose without unnecessary words. It is appropriately sized for the tool's simplicity.

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?

There is no output schema or annotations, and the description does not explain return values, cancellation behavior, or potential errors. The tool has 3 parameters and 2 required, but the description only covers the basic action. More context is needed for an agent to fully understand usage.

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?

The input schema provides 100% descriptive coverage for all parameters, including message, modelId, and conversationId. The description adds no additional parameter context, so the baseline score of 3 is appropriate.

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 action (send a message) and the resource (a DreamingAI conversation), and also notes the expected outcome (get an AI response). This distinguishes it from sibling tools like create_conversation or get_conversation_history.

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 implies a clear use case for interactive messaging, but it does not explicitly state when to use it versus alternatives or mention any prerequisites or exclusions. With no guidance on selecting this tool over siblings, it relies on the agent's inference.

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