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Tech Debt MCP Server

@PierreJanineh

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基本情報

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その他

トランスポート

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作者

PierreJanineh

投稿者

Pierre Janineh

Tech Debt MCP Server について

Static technical-debt analysis across 14 languages, exposed as MCP tools and resources

#mcp#static-analysis#code-analysis#developer-tools#technical-debt#engineering-productivity#ai-tools#llm#model-context-protocol

Tech Debt MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "tech-debt-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "tech-debt-mcp@latest"
      ]
    }
  }
}

Tech Debt MCP Server のツール

16

Analyze an entire project for technical debt. Scans all supported files and returns a comprehensive report with issues, metrics, and recommendations.

Analyze a single file for technical debt issues.

Get a quick summary of technical debt in a project.

Get SQALE technical debt metrics including remediation time, debt ratio, and rating.

List all programming languages supported by the analyzer.

Get prioritized recommendations for addressing technical debt.

Get all issues of a specific severity level.

Get all issues of a specific category.

Add a custom pattern-based tech debt rule.

Remove a custom rule by ID.

List custom rules registered in this server session via add_custom_rule. Does NOT include customPatterns declared in .techdebtrc.json (those run inside analyze_project via AnalysisEngine but are not surfaced here). Renamed from list_custom_rules (TEC-51) for clarity; the old name is no longer registered.

Execute all custom rules against code or a file.

Validate a custom pattern before adding it as a rule.

Analyze project dependencies across multiple package managers.

Validate a .techdebtrc.json configuration file for syntax and schema correctness.

Generate an offline dependency report listing all project dependencies for vulnerability review. Note: actual CVE lookups require Phase 2b online integration.

概要

What is Tech Debt Mcp?

A Model Context Protocol (MCP) server for analyzing technical debt across 14 programming languages. It detects code quality issues, security vulnerabilities, and maintainability problems, and integrates with MCP-compatible tools like GitHub Copilot, Claude, and Cursor.

How to use Tech Debt Mcp?

Add the server to your MCP client configuration using npx -y tech-debt-mcp@latest, or install via one-click links for VS Code, Cursor, Claude Desktop, Windsurf, JetBrains, and Xcode. Once configured, the server exposes 16 tools and 2 resources for analysis, filtering, custom rules, and dependency scanning.

Key features of Tech Debt Mcp

  • Multi-language analysis (14 languages, 10 dependency ecosystems)
  • SQALE metrics with A–E rating system
  • Custom regex-based rules and inline suppression
  • Dependency scanning and offline vulnerability inventory
  • Config validation and flexible filtering by severity/category
  • Security-hardened (path traversal prevention, ReDoS-safe rules, CodeQL scanning)

Use cases of Tech Debt Mcp

  • Analyze an entire project for technical debt across multiple languages
  • Scan a single file for issues on the fly
  • Generate prioritized fix recommendations for a codebase
  • Enforce custom coding standards with user-defined pattern checks
  • Inventory dependencies for CVE review without a network connection

よくある質問

1

What languages does Tech Debt Mcp support?

JavaScript, TypeScript, Python, Java, Swift, Kotlin, Objective-C, C++, C, C#, Go, Rust, Ruby, and PHP.

2

How do I install Tech Debt Mcp?

Use the one-click install links for VS Code, Cursor, or Claude Desktop, or manually add the JSON configuration: `{"mcpServers":{"tech-debt-mcp":{"command":"npx","args":["-y","tech-debt-mcp@latest"]}}}`.

3

Can I define custom rules for tech debt detection?

Yes, you can add regex-based custom rules via the `add_custom_rule` tool, test patterns with `validate_custom_pattern`, and run them with `execute_custom_rules`.

4

Where does Tech Debt Mcp analyze code?

It analyzes files and project directories on the local filesystem, using absolute paths. No code is uploaded externally — all analysis is performed offline.

5

What categories of tech debt does it detect?

Code quality, architecture, documentation, testing, security, performance, maintainability, and dependency issues.

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