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 of behavioral disclosure. It effectively describes key behaviors: the tool blocks execution until completion or timeout, includes polling with configurable intervals, and has a 'compact' parameter that affects output token usage. This covers critical operational aspects, though it could mention error handling or authentication needs.

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 and concise. It starts with a clear purpose statement, followed by a bullet-point-like 'Args' section that efficiently explains each parameter. Every sentence adds value without redundancy, making it easy to scan and understand.

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 polling with multiple parameters) and the presence of an output schema (which handles return values), the description is largely complete. It covers purpose, behavior, and parameters effectively. However, it could benefit from mentioning error scenarios or linking to sibling tools for better context, slightly reducing completeness.

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 0%, so the description must compensate fully. It provides clear semantic explanations for all four parameters: 'notebook_id' as a UUID, 'poll_interval' as seconds between polls with a default, 'max_wait' as maximum seconds to wait with special case for 0, and 'compact' as a boolean affecting report truncation and token savings. This adds significant value beyond the bare 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: 'Poll research progress. Blocks until complete or timeout.' This specifies the verb ('poll') and resource ('research progress'), and the blocking behavior is distinctive. However, it doesn't explicitly differentiate from sibling tools like 'research_start' or 'studio_status', which might have related functionality.

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 the mention of 'notebook_id' and polling behavior, suggesting it's used after initiating research. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'research_start' or 'notebook_query', nor does it 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.