CodeLogic MCP Server Integration

What does “CodeLogic” MCP Server do?

The CodeLogic MCP Server is an implementation of the Model Context Protocol (MCP) designed to provide AI programming assistants with access to CodeLogic’s comprehensive software dependency data. By connecting to this server, AI clients can leverage CodeLogic’s insights to enhance tasks such as code analysis, dependency tracing, and program comprehension. This capability enables developers and AI agents to perform advanced queries on codebases, visualize complex dependencies, and automate workflows that require an understanding of software structure. The server’s role is to act as a bridge between AI systems and CodeLogic’s data, thereby streamlining development processes and improving the efficiency of code-related tasks.

List of Prompts

No information about prompt templates is provided in the repository.

List of Resources

No explicit information about resources is provided in the repository.

List of Tools

• Tool 1:
  • Description not specified. The server implements two tools, but their names and detailed functions are not provided in the available documentation.
• Tool 2:
  • Description not specified.

Use Cases of this MCP Server

• Codebase Analysis
  Enables AI assistants to analyze software projects by accessing detailed dependency data, helping developers understand project structure and identify potential issues.
• Dependency Visualization
  Facilitates the visualization of complex software dependencies, making it easier to comprehend relationships between components and streamline refactoring efforts.
• Automated Refactoring Support
  Assists in identifying safe refactoring opportunities by providing accurate, up-to-date dependency information.
• Impact Analysis
  Supports change impact analysis by tracing dependencies, allowing developers to predict the effects of code modifications before implementation.

How to set it up

Windsurf

1. Ensure prerequisites are met (such as Node.js if needed).
2. Open the configuration file for MCP servers.
3. Add the CodeLogic MCP Server using the following snippet:

   {
     "mcpServers": {
       "codelogic-mcp": {
         "command": "npx",
         "args": ["@codelogic/mcp-server@latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf if required.
5. Verify the setup by checking the MCP server connectivity.

Claude

1. Ensure prerequisites are installed.
2. Locate the MCP server configuration section.
3. Add the CodeLogic MCP Server with:

   {
     "mcpServers": {
       "codelogic-mcp": {
         "command": "npx",
         "args": ["@codelogic/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart the Claude environment.
5. Confirm the server is running.

Cursor

1. Make sure all dependencies are installed.
2. Access the MCP server configuration file.
3. Insert the following configuration:

   {
     "mcpServers": {
       "codelogic-mcp": {
         "command": "npx",
         "args": ["@codelogic/mcp-server@latest"]
       }
     }
   }
   

4. Save and restart Cursor as needed.
5. Test connectivity.

Cline

1. Satisfy all prerequisites.
2. Edit the configuration file responsible for MCP servers.
3. Add the CodeLogic MCP Server config:

   {
     "mcpServers": {
       "codelogic-mcp": {
         "command": "npx",
         "args": ["@codelogic/mcp-server@latest"]
       }
     }
   }
   

4. Save changes and restart Cline.
5. Ensure the MCP server is operational.

Securing API Keys using Environment Variables

To securely store API keys, use environment variables in your configuration. Example:

{
  "mcpServers": {
    "codelogic-mcp": {
      "command": "npx",
      "args": ["@codelogic/mcp-server@latest"],
      "env": {
        "CODELOGIC_API_KEY": "${{ secrets.CODELOGIC_API_KEY }}"
      },
      "inputs": {
        "api_key": "${{ secrets.CODELOGIC_API_KEY }}"
      }
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "codelogic-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “codelogic-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above tables, the CodeLogic MCP Server provides a useful bridge to rich dependency data but lacks detailed documentation on available prompts, resources, and the specifics of its tools. While setup and security are well addressed, further information would increase utility. The repository merits a score of 6/10 for its clarity and open license but loses points for missing details that are essential for advanced integration and use.

MCP Score