detect_membership

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it's an inference operation (not destructive), returns a job_id for asynchronous processing, and mentions three methods (pattern, statistical, combined). However, it lacks details on rate limits, authentication needs, or error handling.

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 appropriately sized and front-loaded, with every sentence earning its place: first sentence states purpose, second specifies inputs and methods, third explains the return value and follow-up action. No wasted words.

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's complexity (asynchronous inference with multiple methods) and no output schema, the description is mostly complete—it explains the purpose, inputs, methods, and that results require polling via check_job. However, it could better address error cases or output format 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 100%, so the schema already documents all parameters well. The description adds minimal value by briefly mentioning the methods (pattern, statistical, combined) and that content_ids are from registered media, but does not provide additional syntax or format details beyond the 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's purpose with specific verbs ('run membership inference', 'determine') and resources ('protected content', 'suspect AI model'), distinguishing it from siblings like detect_ai or detect_fingerprint by focusing on training data detection rather than general AI detection or fingerprinting.

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 provides clear context for when to use this tool (to test if registered media was used in training a suspect model) and mentions an alternative tool (check_job for polling results), but does not explicitly state when not to use it or compare it to other siblings like detect_ai.

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