DeepView MCP

DeepView MCP is a Model Context Protocol server designed to give AI assistants instant, deep access to entire codebases without the need for incremental indexing or on‑the‑fly file fetching. By ingesting a single, AI‑friendly text dump of a repository—typically produced by tools such as repomix—the server stores the whole codebase in memory and exposes it to clients via a single tool. This eliminates the latency that normally accompanies large‑context queries and allows assistants to leverage Gemini’s expansive context window (up to several megabytes) in a single request.

For developers working with IDEs that support MCP—such as Cursor and Windsurf—the server plugs directly into the editor’s workflow. Once configured, a user can issue natural‑language questions about the codebase (e.g., “What functions use ?” or “Show me all files that import ?”) and receive answers that are grounded in the full repository context. The server handles model selection, request routing, and response formatting automatically, so developers can focus on the question rather than on plumbing. The ability to override the Gemini model via command‑line flags or IDE configuration gives teams control over cost, speed, and capability trade‑offs.

Key capabilities include:

• Whole‑codebase ingestion from a single text file, enabling instant full‑project context.
• Gemini model configurability so teams can choose between the fast, low‑cost or the more powerful .
• Integrated MCP tool () that accepts a question and an optional codebase file path, making it straightforward to query the repository from any MCP‑enabled client.
• Logging and diagnostics via a standard log level switch, which aids in troubleshooting performance or model‑related issues.

Typical use cases span code review automation, rapid onboarding of new developers, and AI‑assisted debugging. A senior engineer can ask the assistant to summarize architectural patterns across hundreds of files, while a junior developer can query for the location of a specific dependency. Because the server holds the entire codebase in memory, answers are delivered with minimal latency, and the assistant can reference distant files without additional round‑trips.

What sets DeepView MCP apart is its simplicity and tight integration with existing IDEs. Developers need only generate a output, start the server (or let the IDE launch it automatically), and point their MCP configuration at the executable. The result is a seamless, AI‑powered navigation layer that turns static code repositories into interactive knowledge bases.