deepview-mcp
DeepView MCP is designed for integrating with IDEs to analyze large codebases using the Model Context Protocol. It supports loading codebases from text files and querying them using Gemini's context capabilities. Notably, it offers seamless connectivity with IDEs like Cursor and Windsurf.
What is the primary function of DeepView MCP?
DeepView MCP is designed to analyze large codebases by integrating with IDEs and utilizing Gemini's context window for detailed code insights.
Which IDEs are compatible with DeepView MCP?
DeepView MCP is compatible with IDEs that support the MCP protocol, such as Cursor and Windsurf.
How can I specify a different Gemini model?
You can specify a different Gemini model using the command-line argument --model followed by the model name.
Is it necessary to start the server manually?
No, starting the server manually is not necessary as these parameters are configured in your MCP setup within your IDE.
What are the prerequisites for using DeepView MCP?
The prerequisites include Python 3.13+ and a Gemini API key from Google AI Studio.