validate_llm_response

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

No annotations are provided, so the description must fully disclose behavioral traits. It states validation occurs but does not describe side effects, error handling, return format, or behavior on failure. For a validation tool, this is a significant gap.

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?

Single sentence of 14 words with no redundancy. Every word contributes to defining the tool's purpose. However, it could be expanded slightly to include key behavioral details without sacrificing conciseness.

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 2 parameters, no output schema, and no annotations, the description is too brief. It omits expected return values, error behavior, and validation strength, leaving the agent with insufficient context to use the tool reliably.

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 100% description coverage for both parameters, so the baseline is 3. The description adds context that validation depends on prompt type, which aligns with the enum, but does not elaborate on the response object structure or validation criteria beyond the schema.

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 uses a specific verb 'Validate' and identifies the resource 'LLM response format and content' with context of 'expected schemas for different prompt types'. It clearly distinguishes from sibling tools like browsing or playlist operations, which focus on data retrieval or manipulation.

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?

No explicit guidance on when to use or avoid this tool is provided. While the purpose implies it is for validating LLM responses after generation, there is no mention of prerequisites, alternatives, or exclusions. This is a minimal viable description.

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