llm-output-quality-monitor
Server Details
Cloudflare Workers MCP server: llm-output-quality-monitor
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- lazymac2x/llm-output-quality-monitor-api
- GitHub Stars
- 0
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Tool Definition Quality
Average 3.2/5 across 5 of 5 tools scored. Lowest: 2.4/5.
Each tool targets a distinct aspect of LLM output quality: consistency, drift, hallucination, general quality, and schema. However, quality_validator and schema_enforcer both perform validation, with potential overlap if schema is considered part of structure.
All tools follow a consistent verb_noun pattern in snake_case: consistency_check, drift_detector, hallucination_scorer, quality_validator, schema_enforcer. No deviations.
Five tools is well-scoped for monitoring LLM output quality. Each tool covers a key concern without redundancy, striking a good balance between coverage and simplicity.
The set covers core monitoring aspects: consistency, drift, hallucination scoring, quality validation, and schema enforcement. Minor gaps like bias detection or semantic accuracy could be added but are not critical for the stated purpose.
Available Tools
5 toolsconsistency_checkCInspect
Check consistency across multiple LLM responses
| Name | Required | Description | Default |
|---|---|---|---|
| responses | Yes | Array of responses to compare |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description mentions only the purpose. It does not disclose any behavioral traits, such as whether it is read-only, what the output format is, or any side effects.
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 with no wasted words. However, it is too brief, lacking structure and essential details that could aid an agent.
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's simplicity (one parameter, no output schema), the description is incomplete. It does not explain what consistency entails or what the expected output is, leaving ambiguity.
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 covers the single parameter fully. The tool description adds no additional value beyond the schema, meeting the baseline for high schema coverage.
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 states the tool checks consistency across multiple LLM responses, which is clear but vague. It does not specify what consistency means or differentiate from siblings like drift_detector or hallucination_scorer.
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 on when to use this tool versus alternatives. The description provides no context for selection among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
drift_detectorBInspect
Detect quality drift between current and previous LLM responses
| Name | Required | Description | Default |
|---|---|---|---|
| threshold | No | Drift threshold (0-1, default: 0.15) | |
| currentResponse | Yes | Current LLM response | |
| previousResponse | Yes | Previous LLM response |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral traits. It only states the core function without disclosing details like side effects, required permissions, rate limits, or output nature. This is insufficient for a tool with no annotations.
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, front-loaded sentence with no wasted words. It is very concise, though it could be slightly expanded with minimal overhead. Still earns high marks for efficiency.
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 lack of output schema and annotations, the description fails to explain return values or behavioral outcomes. An agent cannot know what 'detect drift' returns (e.g., a score, boolean, or report). This gaps contextual completeness.
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 has 100% parameter description coverage. The tool's description adds no additional meaning beyond what the schema already provides (e.g., currentResponse, previousResponse, threshold with default). Baseline 3 is appropriate.
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 'Detect quality drift between current and previous LLM responses' provides a specific verb (Detect) and resource (quality drift) with clear context. It effectively distinguishes this tool from siblings like consistency_check or hallucination_scorer.
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 implies usage for comparing two responses but does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use guidance. Context is clear but no exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hallucination_scorerAInspect
Pattern-based heuristic risk scoring for LLM responses (0-100). Detects linguistic signals such as contradictory assertions, unsourced claims, and uncertainty markers. Not a semantic hallucination detector.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Reference context for grounding | |
| response | Yes | LLM response to analyze |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully describes behavior: it uses pattern-based heuristics, outputs a score 0-100, detects contradictory assertions, unsourced claims, and uncertainty markers, and clarifies it is not a semantic hallucination detector. This clearly sets expectations without contradiction.
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 composed of two concise sentences, front-loading the primary purpose and then adding key details. Every word adds value with no extraneous content.
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?
The description explains the score range and detection capabilities, and clarifies limitations (not semantic detector). However, it does not specify the output format (e.g., JSON structure) or how to interpret the score beyond the range. Given no output schema and sibling tools, it is mostly complete but could be slightly more detailed.
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% (both parameters have descriptions). The description adds no additional meaning beyond what the schema already provides for 'context' and 'response'. Baseline score of 3 is appropriate.
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 function ('Pattern-based heuristic risk scoring for LLM responses (0-100)') and lists specific linguistic signals it detects. It also explicitly differentiates itself by stating 'Not a semantic hallucination detector,' distinguishing it from siblings that might detect semantic issues.
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 indicates the tool is for heuristic risk scoring and mentions what it detects, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. It lacks guidance on when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
quality_validatorBInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'validate', implying a non-mutating check, but fails to specify whether it returns a boolean, a detailed report, or error behavior. No mention of side effects or permissions needed.
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, well-front-loaded sentence with no redundancy. Every word adds value, and it is appropriately sized.
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?
The description lacks details about return values or behavior. Without an output schema, it should at least hint at what the validation outputs (pass/fail, score, etc.). It is insufficient for a tool with 4 parameters and no output schema.
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 coverage is 100% with each parameter already described. The description adds generic context ('length, format, and structure') but does not enhance meaning beyond what schema provides. Baseline 3 is appropriate.
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 function: validating LLM response quality based on length, format, and structure. It distinguishes from siblings (consistency_check, etc.) which focus on other aspects, making the purpose specific and non-ambiguous.
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 like consistency_check or schema_enforcer. There are no exclusions, prerequisites, or usage context mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
schema_enforcerCInspect
Validate JSON response against schema
| Name | Required | Description | Default |
|---|---|---|---|
| schema | Yes | JSON schema definition | |
| response | Yes | JSON response to validate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must fully disclose behavior. It only says 'validate', failing to explain outcomes on success/failure, side effects, or whether it is read-only.
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?
Description is a single sentence that conveys the core purpose without waste. Front-loaded and efficient.
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 no output schema and no annotations, the description is too minimal. Should explain validation result format or behavior. Missing return value details.
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 coverage is 100% (both parameters have descriptions in the schema). The description adds no extra meaning beyond what the schema already provides.
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 action (validate) and the target (JSON response against schema). It distinguishes itself from siblings focusing on consistency, drift, etc., but doesn't explicitly differentiate.
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 on when to use this tool versus alternatives like consistency_check or drift_detector. No exclusions or context provided.
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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