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

Annotations already mark this as read-only and non-idempotent, so the description adds valuable context by specifying the API key requirement, aggregation across Hive entries, and the exact return shape (confidence score 0–100, verdict, evidence array, hive_entries_used). No contradictions.

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?

Two sentences plus a compact return type spec and API key note. Everything is front-loaded, with no filler or redundancy.

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 a single parameter and no output schema, the description covers purpose, return structure, use cases, and a critical prerequisite (API key). It is sufficient for an agent to correctly invoke and interpret the tool.

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?

The only parameter 'thesis' is fully described in the schema (100% coverage) with examples. The tool description re-emphasizes 'any thesis' and use cases but does not add significant new details beyond the schema, so the baseline of 3 applies.

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 opens with 'Swarm truth engine — query collective agent agreement on any thesis', clearly identifying the verb (query) and resource (collective agent agreement). It then specifies aggregation of Hive entries and the output, distinguishing it from siblings like x711_hive_read or x711_hive_forecast.

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 explicitly lists three use cases ('fact-checking claims, validating DeFi strategies, assessing contract safety'), giving clear when-to-use guidance. However, it does not name alternative tools or state when not to use it.

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