study_conclude

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

With no annotations, the description carries the burden of explaining behavior. It usefully discloses that negative and abandoned outcomes are valid, successful conclusions, and that linking must happen first. However, it does not explicitly state that this tool finalizes or changes the study's status, nor does it describe any side effects or whether the operation is reversible.

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?

Three sentences, each earning its place: the core purpose, the critical semantic nuance about successful conclusions, and the required upstream workflow. Information is front-loaded and there is no repetition or filler.

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?

For a tool with no annotations and no output schema, the description covers the key conceptual traps an agent could get wrong: negative outcomes count as success, abandoned is valid, and feature links must be recorded beforehand. It does not detail every parameter or consequence, but it gives enough context for correct invocation in most cases.

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 50%, so the description must compensate. It adds useful meaning to recommendation, document_path, and status by framing them around conclusions and abandoned outcomes. However, the required id parameter is not explained, and actor is left undocumented, so the compensation is partial.

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 action and resource: 'Record a study's recommendation and the document it produced.' It also clarifies the scope of a successful conclusion, including negative outcomes like 'Do not build this' and 'abandoned.' This clearly differentiates it from sibling tools like study_open, study_show, and study_outcome.

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 gives clear contextual guidance: use study_conclude to record the recommendation and document, and explicitly instructs the agent to link spawned/killed features with study_outcome before concluding. It does not explicitly state when not to use the tool or name alternatives beyond study_outcome, so it stops short of a 5.

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