research_status

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

With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it blocks execution, has timeout behavior, and includes token-saving considerations with the 'compact' parameter. It doesn't cover all potential behaviors like error handling or authentication needs, but provides substantial operational context.

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 efficiently structured with a clear purpose statement followed by well-organized parameter explanations. Every sentence adds value: the first establishes core behavior, and the parameter descriptions provide essential context without redundancy.

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 (blocking behavior with timeout) and 0% schema coverage, the description does well by explaining parameters and core behavior. Since there's an output schema, it doesn't need to detail return values. It could benefit from more context about what 'research progress' entails or error scenarios.

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 schema description coverage is 0%, so the description must compensate fully. It successfully adds meaning for all 4 parameters: explains 'notebook_id' as a UUID, defines 'poll_interval' and 'max_wait' units and defaults, and clarifies the 'compact' parameter's impact on output truncation and token 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 clearly states the tool's purpose with 'Poll research progress' and specifies it 'Blocks until complete or timeout', which distinguishes it from other tools. However, it doesn't explicitly differentiate from sibling tools like 'studio_status' or 'research_start', which might have related functions.

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 implies usage context through 'Poll research progress' and parameter defaults, suggesting it's for monitoring ongoing research. However, it lacks explicit guidance on when to use this tool versus alternatives like 'studio_status' or 'research_start', and doesn't mention prerequisites or exclusions.

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