Continuous Code Guardian

by ijanzz5621

Overview Schema Related Servers Score Discussions

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Server Quality Checklist

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Latest release: v1.30.1
Disambiguation4/5
Most tools have distinct purposes (design, testing, commenting, documentation, coverage, Sonar preparation), but ca-checkit's meta-tool role may be confused with analysis tools like ca-smellit or ca-sonarit. Descriptions help clarify its routing function.
Naming Consistency5/5
All tools follow the pattern ca-[word]it (e.g., ca-checkit, ca-smellit). The naming is consistent with lowercase and hyphens, and ca-aurait is the only slight outlier but still fits the pattern.
Tool Count5/5
Eight tools cover a comprehensive set of code quality activities without being excessive. Each tool serves a specific subdomain (design, smell, test, comment, doc, coverage, Sonar), and the count is well-scoped for the server's purpose.
Completeness4/5
The tool set covers design, quality, testing, commenting, documentation, coverage, and SonarQube preparation. Minor gaps like security or dependency analysis exist but are implicitly covered by ca-smellit and ca-sonarit, ensuring most workflows are supported.
Average 4.1/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 5 commits in the last 12 weeks
- Last stable release on July 30, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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

Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully covers behavioral traits. It explicitly states that MCP does not write files, call an LLM, or use Cognite certification APIs. It also describes the expected agent workflow of scoring, prompting, and applying fixes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is relatively long and contains some jargon (e.g., 'caps', 'Soft Flows gate') that may not be immediately clear. It is structured with line breaks but could be more concise and front-loaded with the main action.
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?
Given the complexity (5 params, no output schema), the description adequately explains the return values (design_files, rubric) and the overall process. However, the lack of explanation for some parameters reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage from descriptions, and the tool has 5 parameters. The description explains the 'analyzer_mode' parameter with its three options and hints at 'force' usage, but it does not describe 'path', 'focus', or 'workspace_root' meaningfully. This leaves a significant gap in understanding parameter semantics.
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 clearly identifies the tool as a workspace design/UI context pack for design-quality assessment. It specifies scanning the workspace for relevant files and returning design files and a rubric. However, it does not explicitly distinguish itself from sibling tools like ca-smellit or ca-docit, which may overlap in purpose.
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 provides some usage guidance, such as using 'force' for weak signals and noting that non-Flows returns 'not_applicable'. However, it lacks explicit instructions on when to use this tool versus alternatives, and it does not mention any prerequisites or exclusions.
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?
Despite no annotations, the description explicitly states 'MCP does not write files or call an LLM,' clarifies the agent's role (plans edits, asks Proceed), and specifies output structure. It is transparent about limitations (in-source only, no markdown).
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 longer than necessary but every sentence adds value: purpose, parameters, behavior, and workflow. It is front-loaded with the core purpose and structured logically.
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?
Given no output schema, the description adequately explains return values (comment_scorecard, apply_policy) and the agent workflow. However, it lacks detail on parameters 'path' and 'workspace_root', leaving some ambiguity for a tool with 4 parameters and no annotations.
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 0%, so description must compensate. It explains 'focus' (trailing text echoed) and 'analyzer_mode' (none, lint, all). However, 'path' and 'workspace_root' are only implied by 'single-file' context and not described in detail.
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 clearly states it is for 'scorecard-guided in-source comment authoring' with specific outputs (comment_scorecard, apply_policy). While it doesn't explicitly differentiate from siblings, the purpose is specific and actionable.
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 mentions analyzer modes and default skip of heavy analyzers, providing implicit usage context. However, it does not explicitly state when to use this tool over siblings like ca-docit or ca-checkit, nor does it list exclusions.
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?
With no annotations, the description discloses key behaviors: it does not write files, call an LLM, or invoke children. It also clarifies 'invented_metrics is always false' and the default skip of heavy analyzers.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is quite long and includes many details. While front-loaded with purpose, the enumeration of return fields and instructions for the agent could be streamlined.
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?
Given 4 parameters, no output schema, and complex behavior, the description covers most important aspects but lacks detail on 'path' and 'workspace_root', and does not mention error handling or edge cases.
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 0%, so description must add meaning. It explains 'focus' parameter in detail and mentions 'analyzer_mode' values. However, 'path' and 'workspace_root' are not described, leaving gaps.
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 it provides 'evidence-ranked condition-check advising' and lists specific return fields. It distinguishes itself by noting that 'recommendable_tools (never ca-aurait)', which differentiates from sibling ca-aurait.
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 explains when to use it (for context pack advising) and mentions analysis modes. However, it does not explicitly differentiate from other sibling tools like ca-smellit or ca-testit, nor provide when-not-to-use scenarios.
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?
With no annotations, the description discloses key behaviors: it does not write files, call an LLM, install packages, or execute coverage tools. It lists output fields and mentions the tool is a 'source-first readiness prompt.' This provides useful transparency about its non-destructive and preparatory nature.
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 front-loaded with a clear summary and uses bullet-like clarity (lists output fields, analyzer modes). It is somewhat verbose but every sentence adds value. Could be trimmed slightly without losing meaning.
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?
Given no output schema and no annotations, the description covers inputs, behavior, constraints, and output fields sufficiently. It mentions supported languages, measurement examples, and the iterate_policy. A few output fields (source_change_hints, measure_hints) are listed without explanation, but overall it is complete for a tool of this complexity.
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 0%. The description explains 'focus' and 'analyzer_mode' in detail (e.g., focus as free-form guidance, analyzer_mode values). However, 'path' and 'workspace_root' are not explicitly described, leaving ambiguity about their purpose.
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 clearly states it is a 'Fast single-file context pack for coverage-compliance / source readiness preparation.' It distinguishes itself by noting it is NOT a coverage runner and that it generates a prompt for the agent. However, it could more explicitly differentiate from sibling tools like ca-testit or ca-checkit.
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 explains when to use the focus parameter, the default behavior (80% minimum floor, aspire to 100%), and what the tool does not do (write files, call an LLM, etc.). It provides good guidance on invoking the tool via trailing text. However, it lacks a clear when-not-to-use statement relative to sibling tools.
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 provided, so description carries full burden. It discloses behaviors: 'MCP does not write files, call an LLM, or run SonarQube,' and outlines the iterative process. It also explains the analyzer_mode options. It does not explicitly mention destructive actions or auth needs, but the tool appears read-only, so this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose but becomes verbose with procedural details. While informative, it could be more concise. Sentences are long, and some information (like the default max 3 rounds) could be streamlined.
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?
Given the tool's complexity and 0% schema coverage, the description should cover all 4 parameters. It explains focus and analyzer_mode but omits path and workspace_root. The output is listed but not fully detailed. The description provides a good overview of the workflow but lacks complete parameter documentation.
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 0%, so description must compensate. It explains 'focus' (optional free-form guidance) and 'analyzer_mode' (none/lint/all) well. However, 'path' and 'workspace_root' are not described. The description adds value for some parameters but not all.
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 purpose: 'Fast primary-file context pack for SonarQube-style quality-gate preparation.' It uses a specific verb (context pack) and resource (quality-gate prep). It also distinguishes itself from sibling 'ca-smellit' by noting it is not a depth campaign.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly provides usage guidelines: it states when to use ('for quality-gate preparation'), when not to use ('Not a smellit quality-depth campaign — use ca-smellit for that'), and describes the workflow (source-first prep prompt, all-or-nothing proceed, evidence-gated improve→recheck). It also explains that MCP does not run SonarQube.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Thoroughly discloses return fields, that invented_metrics is always false, that MCP does not write files or call LLM, and how focus parameter works (trailing text). No annotations provided, so description carries full burden and meets it.
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?
Front-loaded with key purpose, uses bullet-style listing for clarity. Slightly verbose but each sentence adds value. Could be more streamlined.
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?
Covers return values in detail without output schema, describes main behaviors. Missing parameter explanations for path and workspace_root, but overall fairly complete for a tool with no annotations.
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?
Describes 'focus' and 'analyzer_mode' with details, but 'path' and 'workspace_root' are not explained. Schema coverage is 0%, so description must compensate but is incomplete.
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?
Description clearly states it produces a 'single-file context pack for structured companion markdown' with specific outputs (outline, API extract, drift scorecard). Distinguishes from sibling ca-* tools by specifying its unique purpose and default behavior.
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?
Provides guidance on when to use ('fast', 'default skips heavy analyzers'), analyzer_mode options, and instructions for the agent. Lacks explicit exclusion of alternatives but context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
In the absence of annotations, the description fully discloses behavioral traits: what the tool does (packages context), what it does NOT do (write files, call LLM, run post-apply gate), details about the scorecard (invented_metrics always false), analyzer modes, and soft caps on additional calls.
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 front-loaded with the core purpose and then provides detailed behavioral context. While it is lengthy, each sentence adds necessary information for correct agent usage. It is well-structured but could be slightly more concise.
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?
Given no output schema, the description thoroughly explains what the tool returns (scorecard contents, architecture hints, etc.) and the expected agent workflow. It covers all critical aspects for correct invocation and follow-up actions.
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 0%, so description must compensate. It partially explains analyzer_mode ('none (default, fast heuristic scorecard) | lint (quick) | all (full suite)') but does not explain path, focus, or workspace_root. This adds some value but leaves significant gaps.
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 purpose: to provide a 'current-file context pack for quality-depth + architecture-aware improvement prompts'. It specifies what is packaged (excerpt, findings, scorecard, etc.) and distinguishes itself from the sibling ca-sonarit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to use this tool vs ca-sonarit: 'For Sonar/CI gate prep and optional report-driven iterate, use ca-sonarit instead.' It also outlines the workflow for the agent, including authoring a prompt, asking all-or-nothing proceed, applying splits, and running a post-apply gate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description fully discloses behavioral traits: 'MCP does not write files, call an LLM, or run tests.' It specifies that the agent should merge/append existing test suites unless focus requests rewrite, and that missing runner results in skipped+hints. It limits fix rounds to one and only on listed test paths, and explicitly states 'never edits production.' This is comprehensive and exceeds what annotations would typically provide.
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 dense paragraph that front-loads the purpose and then covers details in a logical flow. Although it is somewhat lengthy, every sentence adds value. Minor improvement could be breaking into bullet points or structured sections for clarity, but it remains effective and not unnecessarily verbose.
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?
Given the tool's complexity (unit test authoring with gap analysis, multiple analyzer modes, and no output schema or annotations), the description provides complete context. It explains the output format (ranked test_gap_scorecard), instructions for the agent (merge/append, verification steps, fix rounds), and even mentions Dart-specific guidance. The recommendation for ca-coverit adds completeness for related workflows.
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 description coverage is 0%, so the description must add meaning. It explains the 'analyzer_mode' parameter with three clear options and their effects: 'none (default, fast heuristic scorecard) | lint (quick) | all (full suite).' For 'path' and 'focus', it hints at their roles via context (tests_root, merge/append vs rewrite). 'workspace_root' is not explained, but overall the description significantly compensates for the lack of schema descriptions.
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 first sentence clearly states the tool's purpose: 'Fast single-file context pack for unit-test authoring + gap scorecard.' It then details the specific outputs (redacted excerpt, tests_root, run_hints, test_gap_scorecard), making the function unambiguous. The mention of ca-coverit as an alternative provides implicit sibling differentiation.
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 gives explicit context for when to use this tool: for unit test authoring with gap analysis. It recommends ca-coverit for coverage-related tasks, providing an alternative. However, it does not explicitly state when not to use this tool or list all scenarios that would favor a different sibling tool.
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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