CodeLogicIncEngineering/codelogic-mcp-server

Overview

This MCP server exposes CodeLogic's software dependency graph to AI coding assistants via the Model Context Protocol. It lets tools like Claude query dependency relationships, impact analysis, and architectural metadata directly from a CodeLogic instance.

Best for

Best for
Teams already using CodeLogic who want to query dependency graphs through an AI assistant

Use cases

• Ask an AI assistant to trace dependency chains before refactoring a module
• Get impact analysis for a proposed code change across a large codebase
• Query which services or libraries depend on a given component

How to use

Tools exposed

• codelogic-method-impact
• codelogic-database-impact
• codelogic-docker-agent
• codelogic-build-info
• codelogic-pipeline-helper
• codelogic-graph-capabilities
• codelogic-graph-search
• codelogic-graph-impact
• codelogic-graph-path-explain
• codelogic-graph-validate-change-scope
• codelogic-graph-owners

Tested with

Cursor, Claude Desktop, VS Code, Windsurf

Example client config

{\n  "mcpServers": {\n    "codelogic-mcp-server": {\n      "type": "stdio",\n      "command": "<PATH_TO_UV>/uv",\n      "args": [\n        "--directory",\n        "<PATH_TO_THIS_REPO>/codelogic-mcp-server-main",\n        "run",\n        "codelogic-mcp-server"\n      ],\n      "env": {\n        "CODELOGIC_SERVER_HOST": "<url to the server e.g. https://myco.app.codelogic.com>",\n        "CODELOGIC_USERNAME": "<my username>",\n        "CODELOGIC_PASSWORD": "<my password>",\n        "CODELOGIC_WORKSPACE_NAME": "<my workspace>",\n        "CODELOGIC_DEBUG_MODE": "true"\n      }\n    }\n  }\n}

Notes

This MCP server exposes CodeLogic’s software dependency graph to AI coding assistants via the Model Context Protocol. It lets tools like Claude query dependency relationships, impact analysis, and architectural metadata directly from a CodeLogic instance.

36 stars on GitHub. Last updated 2026-05-25. Licensed MPL-2.0.

Use cases

• Ask an AI assistant to trace dependency chains before refactoring a module
• Get impact analysis for a proposed code change across a large codebase
• Query which services or libraries depend on a given component

Pros

• Bridges rich dependency data into AI workflows without manual lookups
• Reduces context switching by letting developers ask questions in natural language
• Open source and Python-based, easy to extend or self-host

Cons

• Requires a running CodeLogic instance with indexed codebase data
• Limited to the dependency data CodeLogic has already ingested
• Relatively new project with a small community (36 stars)

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.