Davidyz

VectorCode

Built by Davidyz • 833 stars

What is VectorCode?

A high-performance MCP server implementation for VectorCode that bridges the gap between the Model Context Protocol and external services. It allows Large Language Models to interact with your data and tools with low latency and native support.

How to use VectorCode?

1. Ensure you have an MCP-compatible client (like Claude Desktop) installed. 2. Configure your server with: npx @modelcontextprotocol/VectorCode 3. Restart your client and verify the new tools are available in the system catalog.
🛡️ Scoped (Restricted)
npx @modelcontextprotocol/vectorcode --scope restricted
🔓 Unrestricted Access
npx @modelcontextprotocol/vectorcode

Key Features

Native MCP Protocol Support
High-performance Data Streaming
Type-safe Tool Definitions
Seamless Environment Integration

Optimized Use Cases

Connecting external databases to AI workflows
Automating workflow tasks via natural language
Augmenting developer productivity with specialized tools

VectorCode FAQ

Q

What is an MCP server?

MCP is an open standard that enables developers to build secure, two-way integrations between AI models and local or remote data sources.

Q

Is this server ready for production?

Yes, this server follows standard MCP security patterns and tool-restricted access.

Official Documentation

View on GitHub

VectorCode

codecov Test and Coverage pypi

VectorCode is a code repository indexing tool. It helps you build better prompt for your coding LLMs by indexing and providing information about the code repository you're working on. This repository also contains the corresponding neovim plugin that provides a set of APIs for you to build or enhance AI plugins, and integrations for some of the popular plugins.

[!NOTE] This project is in beta quality and is undergoing rapid iterations. I know there are plenty of rooms for improvements, and any help is welcomed.

<!-- mtoc-start --> <!-- mtoc-end -->

Why VectorCode?

LLMs usually have very limited understanding about close-source projects, projects that are not well-known, and cutting edge developments that have not made it into releases. Their capabilities on these projects are quite limited. With VectorCode, you can easily (and programmatically) inject task-relevant context from the project into the prompt. This significantly improves the quality of the model output and reduce hallucination.

asciicast

Documentation

[!NOTE] The documentation on the main branch reflects the code on the latest commit. To check for the documentation for the version you're using, you can check out the corresponding tags.

  • For the setup and usage of the command-line tool, see the CLI documentation;
  • For neovim users, after you've gone through the CLI documentation, please refer to the neovim plugin documentation (and optionally the lua API reference) for further instructions.
  • Additional resources:
    • the wiki for extra tricks and tips that will help you get the most out of VectorCode;
    • the discussions where you can ask general questions and share your cool usages about VectorCode.
    • If you're feeling adanvturous, feel free to check out the pull requests for WIP features.

If you're trying to contribute to this project, take a look at the contribution guide, which contains information about some basic guidelines that you should follow and tips that you may find helpful.

About Versioning

This project follows an adapted semantic versioning:

  • Until 1.0.0 is released, the major version number stays 0 which indicates that this project is still in early stage, and features/interfaces may change from time to time;
  • The minor version number indicates breaking changes. When I decide to remove a feature/config option, the actual removal will happen when I bump the minor version number. Therefore, if you want to avoid breaking a working setup, you may choose to use a version constraint like "vectorcode<0.7.0";
  • The patch version number indicates non-breaking changes. This can include new features and bug fixes. When I decide to deprecate things, I will make a new release with bumped patch version. Until the minor version number is bumped, the deprecated feature will still work but you'll see a warning. It's recommended to update your setup to adapt the new features.

TODOs

  • query by file path excluded paths;
  • chunking support;
    • add metadata for files;
    • chunk-size configuration;
    • smarter chunking (semantics/syntax based), implemented with py-tree-sitter and tree-sitter-language-pack;
    • configurable document selection from query results.
  • NeoVim Lua API with cache to skip the retrieval when a project has not been indexed Returns empty array instead;
  • job pool for async caching;
  • persistent-client;
  • proper remote Chromadb support (with authentication, etc.);
  • respect .gitignore;
  • implement some sort of project-root anchors (such as .git or a custom .vectorcode.json) that enhances automatic project-root detection. Implemented project-level .vectorcode/ and .git as root anchor
  • ability to view and delete files in a collection;
  • joint search (kinda, using codecompanion.nvim/MCP);
  • Nix support (unofficial packages here);
  • Query rewriting (#124).

Credit

Special Thanks

JetBrains logo.

Star History

Star History Chart

Global Ranking

6.8
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "vectorcode": { "command": "npx", "args": ["vectorcode"] } } }