hypothesis_record

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 explains the behavior (enters as draft, gets reviewed, competes in tournament) and the significance of recording (makes it real). It also mentions that it writes to the same table as Generation and Evolution, adding context. Minor omissions like permission requirements or error handling are not critical for this tool.

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 a single, well-structured paragraph that front-loads the purpose, then explains lifecycle and key parameter usage. Every sentence adds value, no fluff. It is concise yet sufficiently detailed, earning a high score though not as terse as the calibration example.

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 7 parameters (5 required) and no output schema, the description provides enough context for the agent to invoke correctly. It explains the tool's role in the hypothesis workflow, the meaning of key fields, and the distinction between fresh vs evolved hypotheses. The rich schema descriptions complement the text, making the overall documentation complete.

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?

Although the input schema already provides descriptions for all parameters (100% coverage), the tool description adds nuanced guidance beyond the schema: it explains the meaning of parent_ids (derivation vs fresh idea) and strategy (how produced, linking to evolution), and emphasizes that summary is used for dedup/clustering. This enriches parameter understanding significantly.

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: 'Add a hypothesis to a session' and explains its lifecycle (draft, reviewed, tournament). It also distinguishes from siblings like hypotheses_list and hypothesis_get (read tools) and review_record/match_record (review/match tools). The verb-resource pairing is specific and 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?

The description provides clear guidance on when to use parent_ids ('Set parent_ids when it derives from existing hypotheses') and explains the consequence of not recording ('an idea only described in chat is never reviewed'). It implies this is the proper way to register a hypothesis versus just chatting, and mentions the strategy enum values for fresh vs evolution. It lacks explicit 'when not to use' but offers enough context for correct usage.

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