validate_llm_response

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool validates format and content, but doesn't describe what happens during validation (e.g., returns validation results, throws errors, logs issues), what 'expected schemas' entail, or any performance or security considerations. This is a significant gap for a validation tool with zero annotation coverage.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it appropriately sized and well-structured.

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 complexity of validation (involving schemas and multiple prompt types), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., validation results, errors), how schemas are defined, or behavioral aspects. This leaves significant gaps for an agent to use the tool 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?

Schema description coverage is 100%, so the schema already documents both parameters (response and prompt_type) with descriptions and enum values. The description adds marginal value by implying these parameters are used together for validation, but doesn't provide additional syntax, format details, or examples beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 the tool's purpose: 'Validate LLM response format and content against expected schemas for different prompt types'. It specifies the verb (validate), resource (LLM response), and scope (format, content, schemas, prompt types). However, it doesn't explicitly distinguish this validation tool from potential sibling validation tools, though none are listed among the siblings provided.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a generated LLM response), exclusions (e.g., what not to validate), or context for choosing this over other tools. The sibling tools are all Plex/media-related, but no relationship to them is explained.

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