assess_hypotheses

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

With no annotations, the description carries the full burden, and it does disclose the computational method, the observations object shape, and the return contents (posterior percentiles, judgment state, supporting/refuting evidence, reliability). It omits side-effect/permission context and whether any state is mutated, but the methodological and output transparency is substantial.

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 text is compact and front-loaded: the core operation comes first, then the observations format, then the return value. Every sentence carries information, though the parameter block is dense enough to require careful parsing.

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 6-parameter statistical tool with no annotations and no output schema, the description covers the return format and the observations structure but leaves priors, n_samples, and the required pattern argument undocumented. Adequate but with clear gaps.

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% across 6 parameters, so the description must compensate. It thoroughly documents the observations array (evidence_id, state enum, basis enum, reliability range, note) and partly explains benchmark/stage_gap auto-judgment, but leaves priors, pattern, and n_samples entirely unexplained.

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 gives a specific verb+resource: judging competing hypotheses of a pattern using Bayesian updating plus Monte Carlo. It is precise about the operation and method, but does not name or contrast itself with siblings like next_best_evidence or screen_patterns.

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 implicitly routes the agent: evidence_id is resolved via get_catalog('evidence'), and metric/step-linked evidence is auto-judged through benchmark/stage_gap. However, there is no explicit statement of when to use this tool versus alternatives such as screen_patterns or assess_stage.

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