check_hypothesis

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

No annotations are provided, so the description carries the full burden. It usefully discloses what a direct match returns (verdict, positive control, honest bound, anchor) and that two sources are queried, which is real value given no output schema. But it says nothing about auth needs, behavior on no/partial match, or any rate/cost constraints.

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 tight sentences with the purpose question front-loaded and no filler. Some jargon ('honest bound', 'positive control that proves the test was sensitive') costs a little clarity, but nothing is wasted.

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?

With no annotations and no output schema, the description is the sole source of behavioral context and it only partially compensates: return contents are sketched, but the undocumented 'limit' parameter, no-match behavior, and failure modes are unaddressed. 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 67%: 'claim' and 'domain' have descriptions and examples, while 'limit' is undocumented in both schema and description. The description adds no parameter-level meaning beyond the schema, so the baseline of 3 is appropriate.

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?

States a specific verb+resource (check whether an Earth-science hypothesis has been tested) and names the two data sources (Null Museum + Bitcoin-anchored hypothesis registry). It is distinguishable from siblings like list_nulls and run_controlled_test, though it never explicitly names an alternative. Purpose is clear but sibling differentiation is only implied.

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 phrase 'so an agent never re-runs a dead idea' implies the usage context: check before running a test. However, there is no explicit when-not guidance, no named alternative (e.g. run_controlled_test vs verify), and no prerequisites. Usage is implied rather than stated.

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