validate_confidence

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

No annotations are provided, so the description carries full behavioral burden. It explicitly discloses the algorithm (dependency-weighted ceiling), the inflation threshold (>0.15), the falsifiability check, and the rule about confidence ≤0.3 when no falsification condition exists. This is highly transparent and goes beyond a simple operation summary.

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 structured and front-loaded with the purpose. The required input format example is somewhat long but earns its place by reducing ambiguity. Every section serves a distinct role (purpose, input format, rule, algorithm, optional context). Slightly verbose due to the JSON example, but efficient overall.

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 tool has no output schema, so the description should clarify return values. It describes the behavior (computing ceiling, flagging inflation) but does not explicitly state the output format. However, the algorithm description makes the output inferable, and the parameter schema is fully covered. The optional context for iterative enforcement is also explained at a high level, making it complete enough for an agent to use correctly.

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 extra semantic value by providing a concrete JSON example showing the exact structure of assumptions and response_text, and by adding a constraint that falsification_condition is mandatory unless confidence is set to ≤0.3. This goes beyond the schema's basic property descriptions.

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 a specific verb+resource: 'Check whether your claimed confidence is mathematically supported by your assumptions.' It also explains the core function (computes dependency-weighted confidence ceiling, flags inflation, checks falsifiability), which distinguishes it from sibling tools that focus on reasoning chains, numeric claims, or overall quality.

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 context: validate a confidence claim against explicit assumptions with falsification conditions. It provides a required input format and a rule for when to lower confidence (if no falsification condition, set ≤0.3). No exclusions or comparisons to alternatives are given, but the guidance is clear enough to know when to invoke this tool.

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