validate_tool_output

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

With no annotations, the description carries full burden. It describes the two-layer validation (deterministic + Haiku), consensus-based auto-filing, and that it never blocks. It lacks details on the feedback filing mechanism's persistence or potential side effects, but overall transparent.

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 includes some repetitive phrases (e.g., 'Two-layer validation architecture', 'AI-Ready output', 'Token-efficient') that add little new information. It could be more streamlined while retaining essential details.

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 has 3 parameters and no enums, the description covers the core behavior (validation process, consensus, non-blocking). The existence of an output schema reduces the need to explain return values. Minor gap: no mention of error handling or parameter restrictions.

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?

Input schema has 0% description coverage, but the description provides an example that clarifies the parameters: tool_id, query_hash, response_json. However, it does not explain their formats or constraints beyond the example, leaving some ambiguity.

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 states 'Validate any DataNexus tool response for data quality anomalies' with a specific verb (validate) and resource (DataNexus tool response). It clearly distinguishes from sibling tools that focus on compliance, finance, legal, etc., by being a generic validation utility.

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 for validating any DataNexus tool output but does not provide explicit guidance on when to use versus alternatives (e.g., other feedback tools). It mentions 'Never blocks' and 'Always returns structured result' but no exclusions or specific scenarios.

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