median

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explains the empty-list ValueError, the even-length averaging rule, and that the operation and result are logged, which gives the agent useful behavioral expectations beyond the raw schema.

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 well-structured docstring with clear Args, Returns, Raises, and Notes sections. Every sentence provides relevant information and no filler is present.

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 single-parameter calculator tool, the description covers the operation, input contract, return shape, error condition, edge-case behavior, and logging. The presence of an output schema further reduces the need to describe return details.

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% for the top-level parameter, so the description must compensate. The Args section explains that data is an object containing a list of numbers, but this mostly restates the schema's intent without adding deeper semantics such as required format or constraints beyond the empty-list error.

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?

States a specific verb and resource: "Calculate the median of a list of numbers." This makes the tool's purpose immediately clear and naturally distinguishes it from sibling arithmetic and statistical tools like mean or standard_deviation.

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 clearly indicates when to use the tool: whenever a median of a numeric list is required. It does not explicitly mention alternatives or exclusions, but the verb and resource are specific enough that an agent would not confuse it with other siblings.

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