leann-dotnet MCP Server
d-german/leann-dotnet
LEANN .NET — A native .NET 10 MCP server for semantic code search. Chunks source repositories, computes embeddings via ONNX (GPU-accelerated), and serves results over the Model Context Protocol. Designed as a drop-in replacement for the Python LEANN pipeline.
claude mcp add agentrank -- npx -y agentrank-mcp-server Overview
d-german/leann-dotnet is a C# MCP server licensed under MIT. LEANN .NET — A native .NET 10 MCP server for semantic code search. Chunks source repositories, computes embeddings via ONNX (GPU-accelerated), and serves results over the Model Context Protocol. Designed as a drop-in replacement for the Python LEANN pipeline.
Ranked #20 out of 100 indexed tools.
Actively maintained with commits in the last week.
Ecosystem
Score Breakdown
1 stars → early stage
Last commit today → actively maintained
No issues filed → no history to score
1 contributor → solo project
No dependents → no downstream usage
Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →
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From the README
# LEANN .NET A native .NET 10 MCP server for semantic code search. Chunks source repositories, computes embeddings via ONNX (GPU-accelerated), and serves results over the [Model Context Protocol](https://modelcontextprotocol.io/). > **Ported from [LEANN](https://github.com/yichuan-w/LEANN)** by Yichuan Wang — an innovative Python-based vector > database and MCP server for personal AI. The original project pioneered graph-based selective recomputation for > ultra-compact vector indexes (97% less storage than traditional solutions) and supports indexing everything from > codebases to emails to browser history — all locally with zero cloud costs. This .NET port focuses on the > semantic code search and MCP server pipeline, rebuilt natively for Windows/macOS with GPU acceleration via > ONNX Runtime. ### Why a .NET port? The original LEANN requires Python, WSL (on Windows), PyTorch, and several other dependencies — a significant setup burden, especially on corporate machines with limitedRead full README on GitHub →
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