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Kontxt

Kontxt MCP Server is a codebase context analysis service based on the Gemini model, providing codebase understanding tools for AI clients and supporting multiple transport protocols and context attachment functions.
Developer toolsArtificial intelligence chatbots#Code analysis#AI tools#Context service#Gemini integration.Python
2.5 points
6.6K
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Overview

Installation

Content Details

Alternatives

What is Kontxt MCP Server?

Kontxt MCP Server is an intelligent code analysis tool that connects to your local codebase and uses Google Gemini AI technology to help AI clients (such as Cursor and Claude) better understand your code structure, functions, and logical relationships.

How to use Kontxt MCP Server?

Simply set up the server and connect it to your AI client, and you can get intelligent context analysis when querying code. The server will automatically scan the codebase and provide the most relevant code explanations based on your query.

Use cases

It is especially suitable for developers to quickly understand code logic, find relevant function implementations, and analyze system architectures in large codebases.

Main features

Intelligent codebase analysis
Use the Gemini 2.0 Flash AI model to analyze the entire codebase and provide accurate context understanding.
Multi - protocol support
Supports two communication protocols, SSE (recommended) and stdio, to meet the needs of different clients.
Context association
Can perform targeted analysis by combining additional files and context in the user's query.
Usage monitoring
Record API usage in detail to help you optimize queries and control costs.
Advantages
Supports ultra - long context (up to 1 million tokens) and can analyze large codebases
Seamlessly integrates with mainstream AI development tools (such as Cursor)
Flexible configuration options to adjust the analysis depth according to requirements
Detailed API usage statistics for easy cost control
Limitations
Requires a Google Gemini API key
The Windows system needs to install additional tree command support
Analyzing large codebases may take a long time

How to use

Environment preparation
Install the Python environment and activate the virtual environment
Install dependencies
Install the necessary Python dependency packages
Install the tree command
Install the tree command according to your operating system
Configure the API key
Set your Google Gemini API key in the.env file
Start the server
Specify the codebase path to start the server
Client connection
Configure your AI client (such as Cursor) to connect to the MCP server

Usage examples

Understand the codebase overview
Quickly understand the main functions and structure of the entire codebase
Analyze a specific function
Deeply understand the implementation method of a certain function in the codebase
Compare file implementations
Compare the different implementation methods of two files

Frequently Asked Questions

How to get a Google Gemini API key?
What if the tree command cannot be used on the Windows system?
How to monitor API usage?
Why didn't my query get a complete answer?

Related resources

Google Gemini API documentation
The official documentation of the Google Gemini API
Cursor editor
An AI code editor that supports the MCP protocol
Project GitHub repository
The source code of Kontxt MCP Server

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "kontxt-server": {
      "serverType": "sse",
      "url": "http://localhost:8080/sse"
    }
  }
}

{
  "mcpServers": {
    "kontxt-server": {
      "serverType": "stdio",
      "command": "python",
      "args": ["/absolute/path/to/kontxt_server.py", "--repo-path", "/absolute/path/to/your/codebase", "--transport", "stdio"],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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