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

With no annotations provided, the description carries full behavioral burden. It discloses that the tool is equivalent to GET /v1/errors but executed in-process (no HTTP round-trip), which is a meaningful behavioral trait. It also specifies the return structure, including the 'list' object, 'data' array, and 'total' count. This adds valuable context beyond what structured fields would convey.

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 well-structured with clear sections: DESCRIPTION, WHEN TO USE, RETURNS, and EXAMPLE. It is appropriately sized for the information provided—every sentence contributes meaning, from the main purpose to the in-process equivalence to the return payload. The example is concise and illustrative without being redundant.

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 zero-parameter, no-output-schema tool, this description is fully complete. It covers the purpose, usage scenarios, return shape, and underlying API equivalence. The explicit 'RETURNS' section compensates for the lack of an output schema by detailing the exact structure of the response.

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 tool has zero parameters, so per guidelines the baseline score is 4. The description does not need to compensate for missing schema coverage, as there are no parameters to document. The included example call (list_error_codes()) further confirms the no-argument 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 opens with a specific verb+resource statement: 'List every error code in the Trillbacks API error catalog.' This clearly identifies the tool's purpose and scope. It also distinguishes itself from sibling list_* tools by focusing on the error catalog, making it unambiguous.

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 'WHEN TO USE' section explicitly enumerates three concrete use cases (understanding error codes, building client-side error handlers, looking up error types). This provides clear context for when to invoke the tool, though it does not explicitly mention alternatives or exclusions. Sibling tools like list_endpoints are different enough that the context is sufficient.

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