human_list_backends

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

Despite no annotations, the description thoroughly explains that the tool is read-only, lists backends, and details the returned capabilities. No destructive behavior is implied, and the agent can infer safe usage.

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?

Well-structured with sections, front-loaded with purpose. However, there is slight redundancy (e.g., the first two sentences both state the tool lists backends). Still concise and efficient overall.

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 zero parameters and no output schema, the description is fully complete: it describes what the tool does, when to use it, the return schema in detail, includes examples, and provides exclusions.

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?

No parameters are required, and the description compensates by detailing the return structure (id, name, supports_physical/digital, etc.). The schema coverage is 100%, and the description adds significant value beyond the empty schema.

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 tool lists all available backend services and their capabilities. It is distinct from sibling tools like human_dispatch_task and human_list_tasks, which deal with task operations rather than backend discovery.

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?

Explicitly advises when to use: before dispatching a task or debugging routing. Also provides a 'DON'T USE WHEN' section referencing an alternative (set preferred_backends in human_dispatch_task). This is a model for usage guidance.

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