Which integrations are available for this server?

Allows the Hermes agent to use FastContext for codebase exploration.

How do I use fastcontext-hybrid-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@fastcontext-hybrid-mcp show me how the user registration is implemented" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

fastcontext-hybrid-mcp

by LyuboslavLyubenov

Overview Schema Related Servers Score Discussions

Python

Local

FastContext Hybrid MCP Server

An MCP (Model Context Protocol) server that gathers context from codebases using FastContext-1.0-4B-RL — a 4B parameter model trained by Microsoft for repository exploration.

The server combines LLM-guided code exploration with fuzzy matching to find relevant code snippets for any question about a codebase.

How It Works

User question
    ↓
1. DECOMPOSE — break into sub-questions (code-focused + doc-focused)
    ↓
2. EXPLORE — FastContext 4B model searches the codebase via Grep/Glob/Read
    ↓
3. EXTRACT — fuzzy matching extracts only relevant lines from found files
    ↓
4. GAP-FILL — ripgrep + Levenshtein distance catches what the model missed
    ↓
Snippets (~5K tokens) → fed to larger LLM for synthesis

Performance Gains

Why use this pipeline instead of just asking the model directly?

APPROACH COMPARISON (tested on business-auditor, 1170 files)
═══════════════════════════════════════════════════════════════════════════

Method                          Concept     Answerable   Context/Question
                                Coverage
───────────────────────────────────────────────────────────────────────────
Raw FastContext (no pipeline)   50%         3/6          N/A (model output)
+ Path resolution fix           67%         4/6          N/A
+ Hybrid pipeline (unlimited)   97%         6/6          308K tokens
+ Hybrid pipeline (optimized)   92%         6/6           5K tokens  ← this
───────────────────────────────────────────────────────────────────────────

What each layer adds:

Layer                   What it does                            Gain
──────────────────────────────────────────────────────────────────────
FastContext 4B          Finds relevant files via tool calls     Baseline
Query decomposition     Breaks Q into doc + code sub-questions  +17%
Fuzzy snippet extract   camelCase split + Levenshtein matching  +15%
Gap-fill (ripgrep)      Catches what model missed               +25%
──────────────────────────────────────────────────────────────────────
Total: 50% → 92% concept coverage (+84% improvement)

Context efficiency:

Without optimization:  308K tokens/question  (loads full files)
With optimization:       5K tokens/question  (extracts relevant lines only)
Reduction:               62x smaller context

What this means for the larger LLM:

Without pipeline: feed 308K tokens of raw files → exceeds most context windows, expensive
With pipeline: feed 5K tokens of targeted snippets → fits easily, cheap, higher quality

The 4B model handles the expensive exploration work (searching, reading, filtering). The larger LLM only sees the distilled evidence — no noise, no irrelevant code.

Key Features

Smart search: 4B model decides WHERE to look (not just keyword matching)
Fuzzy matching: camelCase splitting, separator normalization, Levenshtein distance
Minimal context: extracts only relevant lines, not full files (~5K tokens vs ~300K)
Gap-fill: ripgrep safety net catches what the model misses
Q4 quantization: runs on 6GB+ VRAM, ~67 tok/s generation

Related MCP server: Moatless MCP Server

Quick Start (Docker)

The fastest way to get started. Docker handles all dependencies.

Note for macOS users: Docker on macOS runs in a Linux VM — it cannot access Metal GPU. For Metal GPU acceleration on Apple Silicon, use the native setup below.

Linux with Vulkan GPU (AMD/Intel/NVIDIA)

git clone https://github.com/LyuboslavLyubenov/fastcontext-hybrid-mcp
cd fastcontext-hybrid-mcp

# Start with your project directory
WORK_DIR=/path/to/your/project docker compose up fastcontext-vulkan

The model downloads automatically on first run (~2.4GB).

CPU-only (macOS Docker or Linux without GPU)

git clone https://github.com/LyuboslavLyubenov/fastcontext-hybrid-mcp
cd fastcontext-hybrid-mcp

WORK_DIR=/path/to/your/project docker compose up fastcontext-cpu

Docker run (manual)

# Build
docker build -f Dockerfile.vulkan -t fastcontext-mcp .

# Run
docker run -d \
  -v /path/to/project:/workspace \
  -v ./models:/models \
  -p 8080:8080 \
  --device /dev/dri:/dev/dri \
  fastcontext-mcp

Quick Start (No Docker — Recommended for macOS)

For native performance with Metal GPU on Apple Silicon, or Vulkan on Linux.

macOS with Metal GPU (Apple Silicon M1/M2/M3/M4)

git clone https://github.com/LyuboslavLyubenov/fastcontext-hybrid-mcp
cd fastcontext-hybrid-mcp
chmod +x setup-mac.sh start.sh

# One-command setup (installs everything, builds llama.cpp with Metal)
./setup-mac.sh

# Start with your project
./start.sh /path/to/your/project

This uses Metal GPU for ~67 tok/s generation. No Docker needed.

Linux with Vulkan GPU

git clone https://github.com/LyuboslavLyubenov/fastcontext-hybrid-mcp
cd fastcontext-hybrid-mcp
chmod +x setup.sh start.sh

# One-command setup
./setup.sh

# Start
./start.sh /path/to/your/project

Manual setup (Linux with Vulkan)

1. Install system dependencies

# Fedora
sudo dnf install cmake gcc-c++ glslc spirv-headers-devel spirv-tools-devel \
    vulkan-headers vulkan-loader-devel ripgrep

# Ubuntu/Debian
sudo apt install cmake build-essential glslc spirv-headers spirv-tools \
    libvulkan-dev ripgrep

2. Install Python dependencies

pip install fastmcp mcp huggingface_hub

3. Build llama.cpp with Vulkan

git clone --depth 1 https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build -DGGML_VULKAN=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j$(nproc)
sudo cp build/bin/llama-server /usr/local/bin/
cd ..

4. Download the model

huggingface-cli download sdougbrown/FastContext-1.0-4B-RL-GGUF \
    FastContext-1.0-4B-RL-Q4_K_M.gguf --local-dir ./models

5. Start

# Start inference server (32K context, 1 slot, Vulkan GPU)
llama-server \
    -m models/FastContext-1.0-4B-RL-Q4_K_M.gguf \
    --ctx-size 32768 \
    --parallel 1 \
    -ngl 99 \
    --host 127.0.0.1 \
    --port 8080 \
    --reasoning off &

# Start MCP server
FASTCONTEXT_WORK_DIR=/path/to/project python3 mcp_server.py

Manual setup (macOS with Metal)

1. Install dependencies

brew install cmake git ripgrep python3
pip install fastmcp mcp huggingface_hub

2. Build llama.cpp with Metal

git clone --depth 1 https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j$(sysctl -n hw.ncpu)
sudo cp build/bin/llama-server /usr/local/bin/
cd ..

3. Download model and start

huggingface-cli download sdougbrown/FastContext-1.0-4B-RL-GGUF \
    FastContext-1.0-4B-RL-Q4_K_M.gguf --local-dir ./models

./start.sh /path/to/your/project

Configure in Hermes Agent

Add to ~/.hermes/config.yaml:

mcp_servers:
  fastcontext:
    command: "python3"
    args: ["/path/to/fastcontext-hybrid-mcp/mcp_server.py"]
    env:
      FASTCONTEXT_WORK_DIR: "/path/to/your/project"
      FASTCONTEXT_SERVER: "http://127.0.0.1:8080"
    timeout: 120

Restart Hermes Agent. Tools appear as mcp_fastcontext_*.

Tools

`search_context`

Main tool — searches a codebase for context relevant to a question.

Args:
  question: str          — The question (conceptual or code-specific)
  work_dir: str          — Path to codebase (optional, uses env var)
  seed: int              — Random seed (default: 42)
  max_turns: int         — Exploration turns per sub-question (default: 6)
  enable_gap_fill: bool  — Use fuzzy gap-fill (default: true)

Returns:
  JSON with:
    snippets: str        — Extracted code snippets (~5K tokens)
    files_read: int      — Number of files explored
    context_chars: int   — Total context size
    keywords: list       — Extracted keywords

`read_snippet`

Extract relevant lines from a single file using fuzzy matching.

Args:
  filepath: str          — Absolute path to file
  concepts: list[str]    — Concepts to search for
  context_lines: int     — Surrounding lines (default: 2)

`list_files`

List files matching a glob pattern.

`health_check`

Check if the inference server is running.

Environment Variables

Variable	Default	Description
`FASTCONTEXT_WORK_DIR`	`/home/llmbox/fastcontext-eval`	Project directory to search
`FASTCONTEXT_SERVER`	`http://127.0.0.1:8080`	llama-server URL
`FASTCONTEXT_MODEL`	`models/FastContext-1.0-4B-RL-Q4_K_M.gguf`	Model path
`FASTCONTEXT_LLAMA_CPP`	auto-detected	llama-server binary path

Hardware Requirements

Backend	Min RAM	GPU	Platform	Notes
Metal	8 GB unified	Apple Silicon M1+	macOS native	Best for macOS — requires native install, not Docker
Vulkan	6 GB	AMD/Intel/NVIDIA	Linux	Mesa or proprietary drivers
CPU	8 GB RAM	None	Any	Works in Docker on any platform, ~10x slower

Performance

Metric	Value
Model size (Q4_K_M)	2.4 GB
VRAM usage	~6 GB (model + KV cache)
Prompt eval	~420 tokens/sec
Generation	~67 tokens/sec
Context per question	~5K tokens
Time per question	~20-40 seconds

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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