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

With no annotations, the description carries full burden. It explains weighting by reputation, prevention of poisoning by low-rep agents, optional signature gates, and return of quorum answer, confidence, and weights. It is transparent but omits potential failure modes (e.g., unachievable quorum).

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 four sentences, front-loaded with the core purpose, and avoids unnecessary detail. It is efficient but slightly verbose with the '~17x' claim. No structural issues.

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 moderate complexity (nested submissions array, optional signature, no output schema), the description covers inputs and key behaviors but lacks detail on return format beyond names, potential errors, or confidence interpretation. Adequate but not comprehensive.

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%, baseline 3. The description adds meaning by explaining the purpose of reputation weighting, the role of signatures in inclusion, and constraints like min/max submissions. This goes beyond the schema's basic types and requirements.

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 computes trust-weighted consensus over agent answers, explicitly using LAWBOR reputation to avoid compounding errors. It distinguishes itself from sibling tools like lawbor_council by focusing on weighted consensus rather than other operations.

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 usage when multiple agent answers exist and one wants weighted consensus, but gives no explicit when-not-to-use guidance or alternatives among sibling tools. The mention of 'fix for ... trap' provides context but not exclusionary criteria.

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