Navigation
DependencyMCP Server: Workflow Automation & Smart Scaling - MCP Implementation

DependencyMCP Server: Workflow Automation & Smart Scaling

DependencyMCP Server: MCP-powered code dependency analysis that automates workflows, slashes dev time, and keeps your projects clean—no guesswork, just smart scaling.

Research And Data
4.7(189 reviews)
283 saves
132 comments

Users create an average of 49 projects per month with this tool

About DependencyMCP Server

What is DependencyMCP Server: Workflow Automation & Smart Scaling?

DependencyMCP Server is a powerful tool designed to automate workflow processes and optimize resource allocation through intelligent scaling. It specializes in analyzing software dependencies, visualizing complex relationships, and ensuring architectural integrity across multi-language projects. By generating dependency maps and providing real-time analysis, it helps teams maintain scalable and maintainable codebases while reducing manual overhead.

Key Features of DependencyMCP Server: Workflow Automation & Smart Scaling

Core capabilities include cross-language dependency tracking for JavaScript, Python, Java, and more, dynamic dependency graph visualization in multiple formats, and automated architecture validation against predefined rules. The server also offers granular file metadata extraction, a scoring system to evaluate code health, and adaptive scaling to handle projects of any size. Its modular design allows seamless integration into CI/CD pipelines, streamlining dependency management at scale.

DependencyMCP Server Features

How to Use DependencyMCP Server: Workflow Automation & Smart Scaling

Start by installing the server via package managers or source code, then configure environment variables for caching and scaling parameters. Use the intuitive API to scan projects, generate dependency maps, and run architecture checks. For deeper integration, leverage the provided SDKs to embed dependency analysis directly into development workflows. The server automatically adapts resource usage based on project complexity, ensuring optimal performance without manual intervention.

Use Cases of DependencyMCP Server: Workflow Automation & Smart Scaling

Common applications include validating monorepo architecture in enterprise systems, identifying circular dependencies in legacy code, and generating compliance reports for regulatory audits. Teams use it to automate dependency updates in CI pipelines, detect version conflicts before deployment, and visualize system interactions for onboarding. It also aids in scaling microservices architectures by mapping cross-component dependencies and suggesting optimal resource allocation strategies.

DependencyMCP Server FAQ

FAQ from DependencyMCP Server: Workflow Automation & Smart Scaling

  • Q: Does it support custom programming languages?
    A: Yes, through plugins. The extensible architecture allows adding language-specific analyzers via community-contributed modules.
  • Q: How does scaling work?
    A: The server dynamically adjusts thread pools and memory allocation based on real-time workload metrics, ensuring efficient resource use even during peak analysis phases.
  • Q: Can it integrate with GitHub Actions?
    A: Absolutely. Prebuilt action templates are available for automated dependency checks on pull requests.
  • Q: What if dependencies change frequently?
    A: The incremental analysis mode reuses cached data to process updates in seconds, avoiding full rescans for minor changes.

Content

DependencyMCP Server

A Model Context Protocol (MCP) server that analyzes codebases to generate dependency graphs and architectural insights. This server helps understand code structure, dependencies, and architectural patterns across multiple programming languages.

Features

  • Multi-Language Support : Analyzes dependencies in TypeScript, JavaScript, C#, Python, and more
  • Dependency Graph Generation : Creates detailed dependency graphs in JSON or DOT format
  • Architectural Analysis : Infers architectural layers and validates against rules
  • File Metadata : Extracts imports, exports, and other metadata from source files
  • Scoring System : Evaluates codebase against architectural rules and patterns

Installation

  1. Clone the repository
  2. Install dependencies:
npm install
  1. Build the project:
npm run build

Configuration

Add to your MCP settings file (usually located at ~/.config/cline/mcp_settings.json or equivalent):

json { mcpServers: { \DependencyMCP: { \command: \node, \args: [\path/to/dependency-mcp/dist/index.js], \env: { \MAX_LINES_TO_READ: \1000, \CACHE_DIR: \path/to/dependency-mcp/.dependency-cache, \CACHE_TTL: \3600000 } } }

Environment Variables:

  • MAX_LINES_TO_READ: Maximum number of lines to read from each file (default: 1000)
  • CACHE_DIR: Directory to store dependency cache files (default: .dependency-cache)
  • CACHE_TTL: Cache time-to-live in milliseconds (default: 1 hour = 3600000)

Available Tools

analyze_dependencies

Analyzes dependencies in a codebase and generates a dependency graph.

const result = await client.callTool("DependencyMCP", "analyze_dependencies", {
  path: "/path/to/project",
  excludePatterns: ["node_modules", "dist"], // optional
  maxDepth: 10, // optional
  fileTypes: [".ts", ".js", ".cs"] // optional
});

get_dependency_graph

Gets the dependency graph for a codebase in JSON or DOT format.

const result = await client.callTool("DependencyMCP", "get_dependency_graph", {
  path: "/path/to/project",
  format: "dot" // or "json" (default)
});

get_file_metadata

Gets detailed metadata about a specific file.

const result = await client.callTool("DependencyMCP", "get_file_metadata", {
  path: "/path/to/file.ts"
});

get_architectural_score

Scores the codebase against architectural rules and patterns.

const result = await client.callTool("DependencyMCP", "get_architectural_score", {
  path: "/path/to/project",
  rules: [
    {
      pattern: "src/domain/**/*",
      allowed: ["src/domain/**/*"],
      forbidden: ["src/infrastructure/**/*"]
    }
  ]
});

Example Output

Dependency Graph (JSON)

{
  "src/index.ts": {
    "path": "src/index.ts",
    "imports": ["./utils", "./services/parser"],
    "exports": ["analyze", "generateGraph"],
    "namespaces": [],
    "architecturalLayer": "Infrastructure",
    "dependencies": ["src/utils.ts", "src/services/parser.ts"],
    "dependents": []
  }
}

Architectural Score

{
  "score": 85,
  "violations": [
    "src/domain/user.ts -> src/infrastructure/database.ts violates architectural rules"
  ],
  "details": "Score starts at 100 and deducts 5 points per violation"
}

Development

The server is built with TypeScript and uses:

  • Zod for schema validation
  • diff for file comparison
  • minimatch for glob pattern matching

Project Structure

dependency-mcp/
├── src/
│   └── index.mts    # Main server implementation
├── package.json
├── tsconfig.json
└── README.md

Adding Support for New Languages

To add support for a new programming language:

  1. Add file extensions to the default fileTypes array
  2. Implement language-specific regex patterns in parseFileImports and parseFileExports
  3. Add any language-specific architectural patterns to inferArchitecturalLayer

License

MIT

Related MCP Servers & Clients