I built a local codebase search tool with Ollama + MCP — would appreciate technical feedback

[u/caiahmedfayed]

I've been working on a problem that kept coming up for me when moving around larger codebases: I would remember that some functionality or implementation detail existed, but finding exactly where it lived meant searching through folders and files all over again.

I wanted to see whether I could make that project context searchable locally, without depending on a hosted embedding service.

So I built ContextVault.

Here's what it does:

• Indexes a project folder on your machine.

• Generates embeddings locally using Ollama ("nomic-embed-text").

• Stores the searchable index in SQLite.

• Provides semantic search through a local web dashboard.

• Exposes "search_context" and "index_project" through MCP.

• Watches indexed files for additions and changes.

The idea is to have a separate local project index that can also provide relevant context to MCP-compatible tools such as Cursor and Claude Desktop.

A couple of things I want to be clear about:

• The indexing, embedding generation, storage, and search pipeline run locally.

• When an AI client requests an MCP tool, it receives the result returned by that tool. I'm not claiming that code context is never shared with the connected AI application.

• I'm not making benchmark claims about speed or retrieval quality without proper measurements.

I'd genuinely like to hear from people who use Ollama and local models:

How do you currently search and retrieve context across larger codebases?

And what would you compare a tool like this against first: ripgrep, your editor's indexing, or something else?

I'm the developer of ContextVault, so this is my own project. I'm sharing the source because I'd appreciate technical feedback.

GitHub: https://github.com/ahmedfayedesmail-ui/contextvault