cuba_juez

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

No annotations exist, so the description fully carries the burden. It discloses the LLM call via subprocess (Claude Code CLI or Anthropic API), cost implications ($0 with subscription), caching (UNIQUE per pair = permanent), and cost control via max_pairs. This is thorough behavioral disclosure.

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?

Four sentences, each earning its place: purpose, context, mechanism, caching. Front-loaded with the core purpose and progressively adds detail. No filler.

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?

The description covers parameters and behavior well, but the absence of an output schema means the description should clarify what the tool returns (e.g., verdict, status). It mentions verdict persistence but not the immediate response. This leaves ambiguity for the agent.

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 100%, so the baseline is 3. The description adds value by explaining the two action modes (judge_pair vs scan_entity), default max_pairs (5), and cost control implications. However, it does not add extra meaning for observation_a/b or entity_name beyond the 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 it is an 'LLM-judge for semantically-conflicting observations' with a specific trigger condition (cosine similarity 0.6-0.8). It defines its scope and differentiates from heuristic detectors, making its unique role apparent even among siblings.

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?

Provides clear usage context: when cosine similarity sits in the ambiguous band and heuristic detectors fail. However, it does not explicitly state when not to use the tool or mention alternative tools for other scenarios.

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