start_behavior_observation

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

With no annotations, the description carries full burden for behavioral disclosure. It only states that the tool starts observation, but does not disclose whether observation is persistent, how it affects performance, or if it is read-only. The word 'observe' weakly implies read-only, but this is insufficient.

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 concise sentence that front-loads the key action and purpose. It is not overly verbose, though it could include more detail without being excessively long.

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 no output schema and no annotations, the description should provide more context about return values or side effects. It does not explain what the tool returns (e.g., an observation ID) or how to stop the observation, leaving the agent uncertain about usage.

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 description does not add meaning beyond the schema. Schema coverage is 100%, so baseline 3 applies. The schema already documents the optional 'windowId' parameter, and the description does not elaborate on its behavior or format.

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 verb 'start' and the resource 'observing AI agent tool calls', with a specific purpose 'to learn usage patterns for policy generation'. This distinguishes it from sibling tools like 'stop_behavior_observation' or 'observation_status', though it could be more explicit about the difference.

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?

No explicit guidance on when to use this tool versus alternatives. The description implies it is a precursor to policy generation, but does not mention when not to use it or provide alternative tools like 'generate_policy_from_observations'.

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