CodeCompass

Overview

CodeCompass is a Model Context Protocol (MCP) server designed to bridge the gap between legacy or sprawling codebases and modern AI assistants. Legacy projects often suffer from tangled dependencies, sparse documentation, and outdated patterns that make it difficult for language models to understand intent or suggest accurate edits. CodeCompass solves this by performing a deep, structured analysis of the repository and then delivering that knowledge to AI tools via Agentic Retrieval Augmented Generation (RAG). The result is a contextual “roadmap” that lets assistants propose changes, refactor code, or answer questions with high relevance and precision.

At its core, CodeCompass indexes the entire repository into a Qdrant vector store. This vector representation captures semantic relationships between files, functions, and modules, enabling fast similarity searches even for large codebases. The server exposes a suite of intelligent tools—such as searching snippets, retrieving full file content with optional summarization, listing directories, and fetching adjacent code chunks—through a single orchestrator tool named . This agent can plan multi-step interactions, deciding when to search for more results or request additional processing time. By integrating diff analysis and dynamic summarization, the server ensures that suggestions stay focused on recent changes while still considering historical context.

Developers benefit from several key capabilities. First, the Agentic RAG workflow means that an AI assistant can request precisely the information it needs, rather than relying on generic prompts. Second, the modular toolset allows custom extensions or replacements (e.g., swapping in a different LLM provider). Third, the server supports both local inference via Ollama and cloud back‑ends like DeepSeek, giving teams flexibility around cost, latency, and data privacy. Finally, the extensive configuration options let users fine‑tune indexing granularity, loop limits, and summarization models to match project size and complexity.

Typical use cases include code review automation, on‑the‑fly refactoring suggestions, documentation generation, and debugging assistance. In a continuous integration pipeline, CodeCompass can surface the most relevant code snippets that relate to a failing test or a newly added feature. In an IDE, the assistant can surface context‑aware suggestions as developers type, reducing cognitive load and speeding up onboarding for new contributors. Because the server is MCP‑compatible, it can be plugged into any AI workflow that supports the protocol, from chat interfaces to task‑oriented agents.

What sets CodeCompass apart is its intelligent orchestration and deep repository understanding. Rather than treating the codebase as a flat text corpus, it models dependencies and structure, enabling nuanced queries that respect module boundaries and historical changes. Combined with the agentic layer, this creates a powerful toolchain that turns any legacy project into an AI‑ready knowledge base, unlocking higher quality suggestions and faster development cycles.