validate_reasoning_chain

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

With no annotations, the description carries the burden and does well: it lists the returned analysis (cycles, orphaned conclusions, grounding_score, enforcement results) and explains that passing context enables escalation and stall detection. It doesn't detail the exact output structure or error handling, but the behavior is well disclosed.

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 opens with the core purpose and then organizes required input, returns, and optional context. The JSON example is long but essential for a graph-shaped input. No filler sentences.

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 no output schema, the description lists all return categories and explains the optional context parameter for iterative use. It could specify the shape of 'enforcement results' more concretely, but overall it covers the tool's complexity effectively.

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 schema already covers all parameters extensively (100% coverage), but the description adds a concrete JSON example and clarifies node types and edge relations beyond the schema enums. It also explains the optional context's role, adding meaning beyond parameter names.

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 first line clearly identifies the action ('Map your reasoning to a directed graph and check it for logical errors') and specifies the error types detected (circular reasoning, unsupported conclusions, orphaned claims). This is a precise verb+resource that distinguishes it from sibling tools focused on confidence or arithmetic.

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 sets clear context for use: validating reasoning chains by converting them into a directed graph. It does not explicitly name alternatives or exclusions, but the focused purpose and input format make the intended usage clear. A slight deduction for not addressing when not to use.

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