get_hypothesis

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

The description adds behavioral detail beyond the annotations by explaining what each view returns, including that the 'context' view drops the raw upstream record server-side and that 'full' includes version history and signal counts. It discloses side effects (dropping duplicate markdown) and the content shape, with no contradiction to the readOnlyHint.

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 compact and well-structured: a one-sentence purpose, a usage note, and a bulleted list of views. Every sentence adds value, and the front-loaded structure makes it easy to scan.

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 has only two parameters and an output schema, the description covers the essential decision points: what each view returns, which is default/recommended, and the scenario for 'full'. It is complete for an agent to select and invoke the tool correctly, and the output schema covers return values.

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 'view' parameter is thoroughly explained with its two enum values, default, and recommendation. The 'hypothesis_id_or_url' parameter is not explicitly described beyond its name and the note that it is used after search_lacuna, which is somewhat implicit given the 0% schema description coverage. However, the name is self-explanatory and the usage guidance fills in the context, so it is only slightly below full compensation.

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 opens with a clear, specific verb+resource statement: 'Fetch a generated novel ML/AI research proposal from Lacuna.' It distinguishes the tool from siblings by focusing on proposals and explicitly ties it to search_lacuna, making its purpose unambiguous.

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?

It provides explicit when-to-use guidance: 'Use after search_lacuna(search_type="hypothesis") to inspect a proposal.' It also explains when to choose each view, recommending 'context' by default and reserving 'full' for cases needing versions or signals, which is strong usage direction.

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