consistency_check
Check consistency across multiple LLM responses
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| responses | Yes | Array of responses to compare |
llm-output-quality-monitor
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are available, so the description must disclose behavior. It only states a pure analysis action without detailing output format, methodology, or resource implications. Since there is no output schema, the agent is left uninformed about what the tool returns.
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, front-loaded sentence with zero fluff. It could add a second sentence with output details without harming conciseness, but it is efficiently worded.
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?
Despite low parameter complexity, the absence of annotations and output schema places a heavy burden on the description. It fails to specify return values or interpretation of consistency, leaving a significant gap for correct invocation.
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?
Schema description coverage is 100% with the parameter 'responses' described as 'Array of responses to compare'. The description adds no additional semantic detail beyond the schema, so the baseline 3 applies.
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?
Description uses a specific verb ('check') and resource ('consistency across multiple LLM responses'), which distinguishes it from sibling tools like drift_detector and hallucination_scorer. However, it does not specify what kind of consistency (e.g., semantic, lexical), leaving slight ambiguity.
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 vs alternatives, prerequisites, or exclusion cases. The description does not mention when consistency checking is preferred over drift detection, quality validation, etc.
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.