DeepView MCP

Implements a Model Context Protocol server that acts as a standardized communication bridge between IDE clients (Cursor, Windsurf) and Google's Gemini API. The server registers a 'deepview' tool that receives user queries, loads preprocessed codebase content from memory, constructs prompts with full codebase context, and returns Gemini's analysis back through the MCP protocol. This eliminates the need for custom IDE plugins by leveraging the standardized MCP specification for tool registration and invocation.

Loads a preprocessed codebase file (typically generated by repomix) into server memory at startup, storing the entire codebase as a single text artifact. When queries arrive, the deepview tool references this in-memory content to construct prompts for Gemini, ensuring the full codebase context is available for analysis without repeated file I/O or API calls to fetch code snippets. This pattern trades memory usage for query latency reduction and eliminates context fragmentation.

Exposes a --model command-line argument that allows users to select different Gemini model variants (e.g., gemini-2.0-flash-lite, gemini-1.5-pro) at server startup. The CLI parser (deepview_mcp.cli:main) passes this selection to the server initialization, which then binds the chosen model to all subsequent API calls via the google-generativeai Python SDK. This enables runtime model switching without code changes, allowing users to trade off latency, cost, and reasoning capability.

The deepview tool constructs prompts by combining the user's natural language query with the entire preprocessed codebase content loaded in memory. The prompt construction logic (in deepview_mcp.server) injects the codebase as context before sending to Gemini, ensuring the model has access to all code when formulating responses. This pattern leverages Gemini's large context window to enable single-turn analysis without requiring the user to manually paste code snippets or provide file references.

Provides a command-line interface (deepview_mcp.cli:main) that parses arguments for codebase file path, model selection, and other configuration options, then initializes and starts the MCP server. The CLI handles argument validation, environment variable resolution (e.g., GEMINI_API_KEY), and server lifecycle management. This pattern enables users to start the server with a single command without editing configuration files or writing Python code.

Supports two distinct query execution paths: direct CLI usage (where users invoke the server and query it from the command line) and IDE integration (where IDEs like Cursor and Windsurf invoke the server as an MCP tool). Both paths use the same underlying deepview tool logic but differ in how queries are submitted and results are returned. The server abstracts these differences, allowing the same codebase analysis engine to serve both interactive CLI users and IDE-integrated workflows.

Integrates with external codebase preprocessing tools like repomix to convert a full repository into a single text file suitable for AI analysis. DeepView expects this preprocessed file as input rather than directly indexing the repository, allowing users to control what code is included, how it's formatted, and what metadata is preserved. This separation of concerns enables flexible codebase preparation workflows while keeping the server focused on analysis.

Integrates with Google's google-generativeai Python SDK to send constructed prompts to Gemini models and receive responses. The server uses the SDK's client initialization (with API key from environment) and model selection to create a generative model instance, then calls the generate_content method with the full-context prompt. This pattern abstracts Gemini API details behind the SDK, handling authentication, model routing, and response parsing.

Implements the Model Context Protocol specification by registering the deepview tool with the MCP server, defining its input schema, description, and handler function. The server uses the mcp Python package to expose this tool to compatible IDEs, which can then discover and invoke it through standardized MCP messages. This pattern ensures compatibility with any MCP-aware IDE without custom integration code.

Formats Gemini's raw response text into a user-friendly format and delivers it through the appropriate channel (CLI output or IDE response message). The formatting logic may include syntax highlighting hints, code block extraction, or structured response organization depending on the delivery mode. This pattern ensures responses are readable and actionable in both CLI and IDE contexts.