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shadrach16

claude-setup-audit-mcp

by shadrach16

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one audits the setup, one scaffolds an agent file, and one returns hook recipes. There is no meaningful overlap or ambiguity between them.

    Naming Consistency4/5

    All names use lowercase snake_case and two follow a verb_noun pattern (audit_claude_setup, scaffold_agent). hook_recipes is noun_noun, so the pattern is mostly consistent with a minor deviation.

    Tool Count5/5

    Three tools is a well-scoped set for an audit-focused server. Each tool earns its place without redundancy or unnecessary surface area.

    Completeness3/5

    The audit tool covers the full setup surface, and there are helpers for agent files and hooks. However, other audited areas like CLAUDE.md, skills, .mcp.json, and .gitignore have no corresponding scaffold or recipe tool, leaving notable gaps for remediation.

  • Average 4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under MIT License.

  • 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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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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are present, so the description carries the full burden. It discloses the main behavior (scoring and listing fixes), which implies a read-only analysis, but it does not explicitly state that no files are modified, nor does it describe any side effects or prerequisites (e.g., whether the path must exist or be a git repo). The core behavior is clear, but transparency is incomplete.

    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 sentences with zero filler. The first sentence front-loads the purpose, scope, and output; the second is a concise input instruction. Every word earns its place.

    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 a single required parameter and no output schema, the description covers the essential information: what the tool checks, what score range to expect, and that fixes come ordered. It could be more explicit about the exact return format or error behavior, but for this simplicity it is adequately complete.

    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%: the path parameter is fully documented as 'Absolute or relative path to the repository root'. The description only repeats this instruction ('Pass the repo root path') without adding new meaning, so the schema already carries the semantic weight.

    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 action ('scores'), the resource ('a repository's Claude Code setup'), and enumerates the specific components (CLAUDE.md, .claude/agents, .claude/skills, hooks in settings.json, .mcp.json, .gitignore). It also specifies the output (0-100 score and ordered fix list). This fully distinguishes it from siblings like scaffold_agent and hook_recipes.

    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 clear context: use this when you want to audit a repository's Claude Code setup and get a prioritized list of fixes. It includes an explicit input instruction ('Pass the repo root path'). It does not explicitly state when not to use it, but the distinct purposes of sibling tools make selection straightforward.

    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 carries the burden of behavioral disclosure. It clearly states that the tool returns recipes, not that it installs or modifies anything, and even adds 'tested' and 'work on Windows and macOS' context. This is transparent for a zero-parameter read-like tool.

    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?

    One sentence, front-loaded with the main action and resource, and compactly lists the three recipe types. Every word earns its place with no fluff.

    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 no-parameter tool with no output schema, the description is complete: it states the number of recipes, their purpose, and platform compatibility. An agent has enough information to invoke the tool correctly and understand what it will get back.

    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 is empty and there are zero parameters, so there is nothing for the description to document. Baseline 4 applies; the description adds useful context about the returned content.

    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 ('Returns') and a clear resource ('three tested settings.json hook recipes'), and names the exact recipes included. It does not explicitly contrast itself with siblings, but its resource and content are specific enough that an agent can tell it apart from audit_claude_setup and scaffold_agent.

    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?

    Usage is implied: call this when settings.json hook recipes are needed. The description does not provide explicit when-to-use guidance, exclusions, or alternatives, though the sibling tools are distinct enough that confusion is unlikely.

    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 carries the full burden of behavior. It clearly discloses that the tool returns text rather than writing the file, instructs the caller to save the output to .claude/agents/<name>.md, and lists the generated file's components.

    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 sentences with no filler: the action and output are front-loaded, and the save instruction is a clear follow-up. Every sentence earns its place.

    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 tool with five parameters all documented in the schema and no output schema, the description adequately explains the return value and next action. It could include more detail on defaults, but the combination of description and schema covers what an agent needs to call it 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 description coverage is 100%, so the schema already documents all five parameters and their meanings. The description does not add parameter-level detail but also does not need to; baseline 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 states a specific action (returns a complete .claude/agents/<name>.md for a role) and enumerates the output components (frontmatter, procedure, report format, do-not rules). This is clearly distinct from the sibling tools audit_claude_setup and hook_recipes, which target different concerns.

    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 intended use is implied by the title and the description of the output: use this when you need to scaffold a subagent file. However, it does not explicitly state when to use this tool versus alternatives or provide exclusion conditions.

    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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