Summarize (pydantic-ai structured output + sampling)

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

With no annotations present, the description carries the burden of behavioral disclosure. It does usefully disclose that the LLM comes from the client via MCP sampling and that output is structured, which is meaningful. However, it does not mention side effects, failure modes, rate limits, or any constraints beyond that.

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 compact sentence with no filler. It front-loads the primary result (structured Summary) and adds the sampling detail in a second clause, making it efficient.

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 one-parameter tool with an output schema, the description is close to adequate, but it misses an explicit statement that the tool summarizes the given text. It also offers no usage guidance, so an agent must rely on the tool name and schema to understand the full contract.

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 0%, and the description does not mention the `text` parameter at all. The parameter name is self-explanatory, but the description does not compensate for the lack of schema documentation by explaining expected format, length, or usage.

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 says a pydantic-ai agent returns a structured Summary, which hints at the action, but it never explicitly states that it summarizes the provided text. The tool name and `text` parameter make the purpose inferable, but the description itself is more about the internal mechanism than the user-facing operation.

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?

There is no guidance about when to use this tool, when not to use it, or which alternative to consider. It only describes the mechanism of structure generation and LLM sampling, not the invocation context.

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