record_hypothesis

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

With no annotations, the description carries the burden. It reveals that hypotheses are tracked in the knowledge graph, can be confirmed/falsified by bench results, and that the tool returns the hypothesis ID. This provides meaningful behavioral context beyond a simple 'record' verb.

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 front-loaded with the core purpose. Each section (context, args, returns) earns its place, and the example for statement is concise and illustrative. No redundancy or fluff.

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 moderate complexity (4 params, no annotations, no output schema), the description is complete: it states purpose, usage context, parameter semantics, and return value. It covers all necessary information for invocation.

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 coverage is 0%, so the description fully compensates by explaining every parameter: statement with an example, assumptions as comma-separated, test_plan as verification method, and related_concepts as IDs to link. This adds rich semantic meaning 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 records a testable hypothesis about a circuit or design issue, and distinguishes it from sibling tools like record_decision or record_bench_result by specifying the knowledge graph tracking and confirmation/falsification by bench results.

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 explicitly says 'Use this when exploring root causes, predicting behavior, or proposing explanations,' providing clear usage context. It does not explicitly name alternatives or exclusions, but the guidance is sufficient for an agent to choose it appropriately.

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