anthropic_create_message

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

With no annotations provided, the description must fully disclose behavior. It omits important details such as side effects (none expected), authentication requirements (api_key needed but not highlighted), rate limits, error handling, or response format. This lack of transparency could lead to unexpected agent behavior.

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 extremely concise with two sentences. The first sentence directly states the core functionality, and the second adds context. No unnecessary words or repetition, making it efficient for agents to parse.

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 tool has 10 parameters and no output schema or annotations, the description is insufficient. It fails to explain how parameters interact, required fields beyond api_key, expected output, or error conditions. An agent would need additional context to use this tool reliably.

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 already describes 60% of parameters (e.g., model, prompt, messages, system, max_tokens). The description adds no extra semantic value beyond what the schema provides, so it meets the baseline for moderate coverage.

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 target resource ('Anthropic Messages API (Claude models)'). It identifies the tool as a means to call Claude programmatically, which differentiates it from sibling LLM tools like OpenAI or Groq, though not explicitly naming alternatives.

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 gives a use case ('agents that need to call Claude programmatically or compare model outputs'), implying when to use it. However, it does not mention when not to use it or explicitly compare to sibling LLM tools, leaving room for ambiguous selection.

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