Context-first

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

With no annotations provided, the description carries full burden and does well: it discloses the tool's multi-method approach, auto-selection behavior, cascade functionality for correction, and scoring threshold (below 0.5 triggers correction). It explains the tool's operational logic beyond basic input-output, though it could mention performance characteristics or error handling more explicitly.

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 appropriately sized for a complex tool with 4 parameters and 7 methods. It front-loads the 7 tools list, then explains auto-selection and cascade behavior. While dense, every sentence adds value; it could be slightly more structured but remains efficient without wasted words.

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 the tool's complexity (4 parameters, nested objects, 7 methods) and no annotations/output schema, the description does well: it covers purpose, usage modes, parameter effects, and behavioral logic. It explains the multi-tool approach and cascade correction, though it doesn't detail return formats or error cases, which would be helpful given the absence of output schema.

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 100%, so baseline is 3. The description adds meaningful context: it explains that 'check' overrides auto-selection and lists what each enum value represents (e.g., 'probe — linguistic truth proxy signals'), providing semantic clarification beyond the schema's technical descriptions. It also explains the cascade parameter's effect, adding operational understanding.

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 explicitly states the tool's purpose: it performs truthfulness checking using 7 specific methods (probe, truth_direction, ncb, logic, verify_first, ioe, self_critique). It distinguishes itself from siblings by focusing on truth verification rather than context management, reasoning, or file operations. The description provides a clear verb ('truthfulness checking') and resource ('7 tools') with specific differentiation.

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 provides explicit guidance on when to use this tool: it mentions auto-selection of methods or manual override with 'check' parameter, and specifies cascade=true for auto-correction on low scores. It distinguishes usage scenarios between automatic and manual modes, though it doesn't explicitly mention when NOT to use it or alternatives among siblings, but the context is sufficiently clear for a complex tool.

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