schema_enforcer
Validate JSON response against schema
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| schema | Yes | JSON schema definition | |
| response | Yes | JSON response to validate |
llm-output-quality-monitor
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It states the validation action but does not describe what happens on success/failure, whether it returns a boolean or errors, or any side effects. This leaves significant behavioral ambiguity, similar to the 'update_drive' example.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single terse sentence, front-loaded with the verb 'Validate'. It is concise but lacks any supplementary structure or context; while it wastes no words, it could benefit from brief usage or behavioral notes without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 required parameters, no output schema, no annotations), the description is incomplete. It does not explain return values, error behavior, or how this tool fits into the validation workflow. The lack of an output schema further increases the need for description to clarify outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already defines both parameters with descriptions, achieving 100% coverage. The description adds no additional parameter-specific information, such as how the schema object is structured or how the response string is parsed. Baseline 3 applies given high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Validate JSON response against schema' clearly states the tool's function with a specific verb and resource. It distinguishes from sibling validation tools by focusing on schema conformance rather than consistency, drift, hallucination, or quality. However, no further details about the validation process or return value are provided, so score 4.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. The description does not mention situations where schema validation is preferred, nor does it reference sibling tools or exclusions. The agent has no context for tool selection beyond the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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TDQS
B3.2/5.0
Disambiguation3/5
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.
Naming Consistency3/5
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.
Tool Count5/5
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.
Completeness4/5
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.