contradiction_candidates

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

No annotations are provided, so the description carries the disclosure burden. It does disclose meaningful behavior: output is grouped by subject, and hotspots indicate one subject holding several values for a relation. However, it does not state whether the operation is read-only, how results are bounded, or what happens when all parameters are null.

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 two concise sentences with no filler or repetition of the tool name. The core output behavior is front-loaded, and the follow-up instruction to judge candidates earns its place as actionable guidance.

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 three optional parameters, no annotations, and no output schema, the description is not complete enough for reliable invocation. It leaves unresolved what dataset selects, whether entity_names and relation_ids are alternatives or combined filters, and what an empty call returns. The grouping and hotspot summary is useful but insufficient as a standalone guide.

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 description coverage is 0%, so the description must compensate. It clarifies that entity_names and relation_ids select the entities or relations around which facts are gathered. However, it never mentions the dataset parameter, nor does it explain how the parameters combine, default, or behave when null.

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 identifies the tool as providing facts around given entities or relations, grouped by subject, with hotspots for multiple values on one relation. This distinguishes it from generic recall or history tools. It lacks an explicit verb like 'retrieve' or 'list', but the intent is not ambiguous.

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 implies the tool is used to surface contradiction candidates and directs the agent to judge them with the mnemoth-contradictions skill, giving a clear follow-up step. However, it does not explicitly state when to prefer this over siblings like memify_candidates or recall, nor does it describe any exclusions.

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