Validate LLM response quality based on length, format, and structure
| Name | Required | Description | Default |
|---|---|---|---|
| response | Yes | LLM response to validate | |
| maxLength | No | Maximum response length (default: 10000) | |
| minLength | No | Minimum response length (default: 10) | |
| strictFormat | No | Enforce punctuation and capitalization |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only states what is being validated, not what the tool returns, how it signals validation failure, or whether it has side effects. It omits critical information about the output format (e.g., score, boolean, or error details).
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 sentence that is front-loaded with the verb and includes no filler. It efficiently conveys the core function without unnecessary detail.
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 multiple parameters and no output schema, the description is incomplete: it does not explain return values, usage context, or edge cases. It fails to provide enough information for an agent to confidently invoke the tool and interpret the result.
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 input schema already describes all four parameters with 100% coverage, so the baseline is 3. The description adds general context but no extra parameter-specific meaning beyond what the schema provides, such as how strictFormat relates to 'format' or how maxLength/minLength map to 'length'.
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' with a clear resource 'LLM response' and specifies the scope 'length, format, and structure', which distinguishes it from sibling tools like consistency_check, drift_detector, hallucination_scorer, and schema_enforcer. This makes the core purpose unambiguous.
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 guidance is provided on when to use this tool versus alternatives. There is no mention of sibling tools, exclusions, or conditions that would trigger its use; the agent must infer from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
The tools are mostly distinct, but consistency_check and drift_detector both involve comparing responses, and quality_validator and schema_enforcer overlap on validation. The descriptions help clarify boundaries, especially for hallucination_scorer which is clearly unique.
Most names follow a noun_noun pattern (drift_detector, hallucination_scorer, quality_validator, schema_enforcer), but consistency_check deviates by using a verb as the second element. All are snake_case, so the overall style is recognizable but not perfectly uniform.
Five tools is well within the optimal range for a focused monitoring server. Each tool addresses a distinct quality aspect without unnecessary bloat or sparsity.
The toolset covers key monitoring dimensions: single-response quality, schema validation, hallucination risk, cross-response consistency, and time-based drift. Missing semantic hallucination detection is acknowledged as a limitation, but it's a minor gap given the scope.