qc-validator-mcp

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation4/5
The tools are mostly distinct: validate_output provides general scoring, while check_hallucination_risk and check_scope_compliance target specific failure modes. There is slight overlap between validate_output's forbidden patterns and check_scope_compliance's allowed/forbidden topics, but the descriptions clarify different scopes.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (validate_output, get_failure_patterns, check_hallucination_risk, check_scope_compliance, log_validation, generate_quality_report), using lowercase with underscores throughout. This makes the API predictable and easy to navigate.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of validating agent output. Each tool covers a distinct step in the validation workflow: executing checks, analyzing patterns, logging results, and reporting, without unnecessary bloat.
Completeness4/5
The core workflow (validate, log, analyze, report) is covered. However, there is no tool to manage the configurable criteria mentioned in validate_output, which is a minor gap. Additionally, update/delete operations for logged results are missing, but the main lifecycle is intact.
Average 3.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
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- No high-severity vulnerability alerts
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description alone must disclose behavior. It reveals what rules are checked, but it does not mention what the return value or result format looks like, possible side effects, error behavior, or whether it is a read-only operation. For a validation tool, this is a substantial gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences, front-loaded with a clear purpose, followed by a compact enumeration of checks. There is no filler or irrelevant information.
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?
There is no output schema or annotations, so the description has the full burden of informing the agent of the tool's outcome and behavior. It fails to explain what the tool returns (e.g., boolean, report, throws error) and does not discuss limitations or edge cases, leaving the agent uncertain about post-call handling.
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%; both parameters and all nested properties are described. The description's mention of 'word limits, required sections, allowed/forbidden topics' merely restates the schema and adds no deeper semantic value, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 specific resource ('agent output') and a specific criterion ('defined scope contract'). It explicitly lists the types of checks (allowed/forbidden topics, word limits, required sections), which distinguishes it from sibling tools like validate_output or check_hallucination_risk.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need to check output against a scope contract, but it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. No contrast with sibling tools is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It adds minimal context by stating that statistics accumulate over time, which hints at persistence and aggregation, but it fails to disclose whether the operation is idempotent, whether duplicates are allowed, what happens on repeated calls, or any permissions or rate limit implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, two sentences long, and front-loaded with the primary action 'Store'. Every sentence contributes meaningful context about the tool's purpose and aggregation behavior, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a moderate-complexity storage tool with well-documented parameters but no output schema or annotations. The description conveys the core purpose but omits details such as expected return values, error conditions, or whether the operation is synchronous. Given the availability of sibling tools, more context on when this fits the workflow would improve completeness.
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 provides complete descriptions for all five parameters with 100% coverage, so the baseline is 3. The description adds no additional parameter context, but since the schema already documents each field's meaning, no further clarification is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool stores a validation result, with the specific purpose of enabling trending and failure pattern analysis. The verb 'store' plus the resource 'validation result' and the context of accumulating per-agent statistics distinguish it from sibling tools like validate_output or get_failure_patterns, which perform validation or retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for recording validation outcomes to enable later analysis, but it does not explicitly state when to use this tool versus alternatives. No exclusions or conditions are mentioned, and sibling tools like validate_output or get_failure_patterns are not referenced for differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It transparently states that the tool evaluates output quality and lists the dimensions it checks, giving the agent a clear sense of its non-mutating evaluation behavior. However, it does not describe the return value format, whether any side effects exist, or how scores are computed, leaving significant behavioral details undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The first sentence front-loads the primary purpose, and the second efficiently enumerates the specific checks. Every phrase adds useful information, and it is easy to scan quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description gives a solid overview of what the tool evaluates, but the absence of an output schema and annotations means the agent is left without key context such as the format or meaning of the returned score, thresholds, or how to interpret results. Given the nested criteria object and no output schema, more detail on the output would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds value by summarizing how the parameters map to high-level checks (e.g., 'claim density' corresponds to factual_claims_count and 'task relevance' relates to task_description), making the semantics more accessible. This goes beyond simply restating the schema.
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 uses a specific verb ('Score') and clearly identifies the resource ('agent output quality') and the configurable criteria. It lists concrete checks (length, keywords, forbidden patterns, claim density, task relevance), which makes the purpose clear. However, it does not explicitly distinguish this tool from sibling tools like check_scope_compliance or check_hallucination_risk, so it falls short of full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use the tool by enumerating the checks it performs, so an agent could infer it is appropriate for general output validation against criteria. However, it provides no explicit guidance on when not to use it or what alternatives exist (e.g., check_hallucination_risk for specific hallucination checks). The context is implied rather than clearly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. The verbs 'Analyze' and 'Returns' imply a read-only operation, and it does list what it returns. However, it does not disclose any side effects, required permissions, rate limits, or the meaning of 'quality trend direction'—leaving some behavioral uncertainty.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the main action and lists outputs concisely. Every word contributes value, with no fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool with no output schema, the description adequately covers the core purpose and return values. It could be more complete by defining 'failure modes' or 'trend direction,' but overall it gives sufficient context for an agent to invoke the tool correctly.
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 coverage is 100%: the parameter agent_id is fully described as 'Agent identifier to analyze'. The description only reiterates 'specific agent' without adding new semantic meaning beyond the schema, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with 'Analyze common failure modes for a specific agent' and lists concrete outputs (pass rate, average score, frequent issue types, trend direction). This specific verb+resource combination distinguishes it from sibling tools like validate_output or generate_quality_report.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('for a specific agent') but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. It relies on the reader to infer that this tool is for analyzing individual agent failure patterns, which is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It explains the two operating modes (grounding check vs. heuristic flagging) but does not describe the output format, side effects, or any limitations. It adds some transparency but leaves notable gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences, front-loaded with the primary purpose and then a clear conditional. No wasted words; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 3 parameters, no output schema, and no annotations. The description explains the tool's core behavior and the conditional use of source_text, but does not specify what the tool returns (e.g., a risk score, a flag, a report). This missing output information makes it less complete than ideal.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining how source_text changes the tool's behavior ('checks grounding') and implying the role of claim_count as a threshold. This goes beyond simple parameter listings.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool estimates 'hallucination likelihood in agent output,' which is a specific verb+resource. It distinguishes from sibling tools like validate_output or check_scope_compliance by focusing on hallucination risk, not structural or scope validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool (for hallucination risk) and explains two conditional modes based on whether source_text is provided. It doesn't explicitly mention alternatives or exclusions, but the conditional behavior gives practical usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full responsibility. It discloses what the tool does and what the output contains, which is sufficient for a zero-parameter dashboard generator. It does not mention potential side effects or prerequisites, but these are unlikely to be significant.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The first sentence states the core action, and the second enumerates the outputs. Both sentences add value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter tool with no output schema, the description is highly complete. It tells the agent exactly what the tool does and what the results contain, leaving no ambiguity about invocation or expected response. Minor gap: no explicit mention of when to use vs. sibling tools, but that's already accounted for in usage guidelines.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the description correctly omits parameter details. Based on the rubric, a baseline of 4 is appropriate for tools with no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as generating a quality dashboard for all validated agents, listing specific output components (per-agent summaries, pass rate, performers, recommendations). This distinguishes it from sibling tools like validate_output or get_failure_patterns, which focus on individual validation or failure analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for obtaining a holistic quality overview after validation, but it does not explicitly contrast it with alternatives or state when not to use it. However, the output details make the intended usage clear enough.
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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