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amirmadani

senior-sde-mcp-guard

by amirmadani
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools are distinct in their input: check_code_quality presumably audits code provided directly, while audit_file reads a file from disk. However, both perform the same core action (auditing against AST rules), causing minor potential for confusion if the input types are not obvious.

    Naming Consistency5/5

    Both tool names follow a clear verb_noun pattern (check_code_quality, audit_file). The verbs are different but semantically similar, and the structure is consistent, making the naming predictable and easy to parse.

    Tool Count3/5

    With only two tools, the server feels thin for its stated purpose of code quality auditing. It covers direct code and files, but the minimal count suggests a limited scope that may not handle broader workflows.

    Completeness3/5

    The domain is code quality auditing, and the tools cover auditing code snippets and files. However, there is no tool to list or retrieve the AST rules, adjust audit scope (e.g., directories), or provide remediation guidance, leaving notable gaps in the workflow.

  • Average 3.8/5 across 2 of 2 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
    • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    Since annotations are absent, the description carries the full burden of behavioral transparency. It discloses the specific rules checked, which adds context, but it does not mention side effects (e.g., read-only behavior), return format, or failure modes. This is adequate but not deeply transparent.

    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, well-structured sentence that front-loads the action and lists the audit rules without redundant wording. Every phrase contributes to understanding the tool's purpose.

    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?

    The description, combined with a complete parameter schema and an output schema (which likely documents return values), covers the essential aspects of the tool. However, the lack of usage guidance relative to 'audit_file' and the absence of behavioral details (e.g., side effects) prevent a perfect score.

    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 both parameters (file_path and code_content) clearly described in the schema. The tool description adds no additional meaning to either parameter, so the baseline score of 3 is appropriate.

    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 verb 'Audits' clearly specifies the action, and the description enumerates specific AST rules such as Class size, Complexity, and Secret leakage, making the tool's scope evident. However, it does not explicitly contrast with the sibling tool 'audit_file' beyond the rule list, so it falls short of a 5.

    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 the tool is for checking code quality against predefined AST rules, but it provides no explicit when-to-use or when-not-to-use guidance. With a sibling tool 'audit_file' present, the absence of any differentiation or alternative mention limits the guidance to implied usage.

    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 transparency burden. It discloses a read operation ('Reads') and the audit action, but does not mention side effects, error handling, or whether it modifies anything. The behavior is partially transparent but lacks depth.

    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 is direct and free of filler. It front-loads the core action and purpose, earning every word.

    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 low parameter count and an output schema that likely describes return values, the description sufficiently covers the tool's core behavior. It could mention prerequisites or limitations (e.g., file must be in workspace), but overall it is complete enough for a simple tool.

    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 schema describes 'file_path' as a path on disk. The description adds meaning by specifying 'workspace file' and 'existing', which constrains the parameter beyond the generic schema. This extra context is valuable.

    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 specific verbs 'Reads' and 'audits' with a clear resource ('existing workspace file') and specifies the audit against 'Senior SDE AST rules'. This distinguishes it from the sibling tool by naming the specific rule set, making the purpose unmistakable.

    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 auditing files against AST rules, but it does not state when to prefer this tool over the sibling 'check_code_quality' or any exclusions. Usage context is present but not explicit.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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