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

Beyond the annotations, the description discloses that raw input is deleted at job completion, results expire within 24 hours, the operation creates only temporary state, and it never acts on a vendor or customer account. This adds meaningful behavioral context that the annotations alone do not convey.

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 concise, front-loaded with the core purpose, and every sentence adds value: the action, the immediate return, the follow-up mechanism, and the data lifecycle. No filler or repetition.

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, output schema, and sibling set, the description covers all essential operational aspects: what it returns, how to track it, data expiration, and side-effect boundaries. An agent has enough context to invoke it correctly and know what to expect.

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 80%, so the schema already documents most parameters. The description adds no new parameter-level detail, but it does not need to significantly compensate; the baseline of 3 is appropriate.

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 states a specific verb and resource: it creates and launches an idempotent temporary MCP study for any supported Boncourant analytical tool. It clearly distinguishes itself from sibling analytical tools by emphasizing that it returns a receipt rather than the analysis result itself.

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 clearly indicates this tool is for launching an asynchronous study and explicitly directs users to get_analysis_study for tracking. It does not explicitly enumerate when-not-to-use cases relative to the direct analytic siblings, but the intended workflow is evident.

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