Fuel Network & Sway Language
Semantic search for Fuel Network and Sway Language documentation using a local vector database.
Fuel Network & Sway Language MCP Server
This project provides a Model Context Protocol (MCP) server for the Fuel Network and Sway Language ecosystem. It allows IDEs (like Cursor) to search and interact with Fuel documentation directly within the development environment.
The server indexes Fuel and Sway documentation into a local Vectra vector database using open-source embeddings (via Transformers.js) for powerful semantic search capabilities.
Features
- Local semantic search of docs.fuel.network content
- No Docker dependency - runs with just Bun
- Fast file-based vector storage with Vectra
- Enhanced result filtering and formatting
- Hybrid search with keyword fallback
Quick Install
# Clone the repo
git clone https://github.com/FuelLabs/fuel-mcp-server
cd fuel-mcp-server
# Install dependencies
bun install
# Index documentation
bun run src/indexer.ts ./docs
# Test search
bun run src/query.ts --run "What is FuelVM?"
# Start MCP server
bun run src/cli.ts
Usage
STDIO Transport (Default)
bun run src/cli.ts
# or explicitly
bun run src/cli.ts --transport stdio
HTTP Transport
bun run src/cli.ts --transport http --port 3500
# Server runs at http://127.0.0.1:3500/mcp
# Health check: http://127.0.0.1:3500/health
CLI Options
bun run src/cli.ts --help
bun run src/cli.ts --transport http --port 3500
bun run src/cli.ts --transport stdio
Claude/Cursor Integration
Add to your MCP config file:
{
"mcpServers": {
"fuel-server": {
"command": "bun",
"args": ["run", "/absolute/path/to/fuel-mcp-server/src/cli.ts", "--transport", "stdio"]
}
}
}
Project Structure
.
├── docs/ # Markdown documentation files
├── src/
│ ├── cli.ts # Main CLI entry point
│ ├── server.ts # MCP server factory
│ ├── transports/
│ │ ├── stdio.ts # STDIO transport
│ │ └── http.ts # HTTP transport
│ ├── chunker.ts # Markdown chunking logic
│ ├── indexer.ts # Document indexing script
│ ├── query.ts # Search query script
│ └── *.test.ts # Test files
├── vectra_index/ # Local vector database (created after indexing)
├── package.json
└── README.md
Prerequisites
- Bun: Install from bun.sh
Usage
1. Index Documents
Place markdown files in ./docs or specify a different directory:
# Index docs in ./docs (default)
bun run src/indexer.ts
# Index custom directory
bun run src/indexer.ts /path/to/your/docs
# With custom settings
EMBEDDING_MODEL=Xenova/bge-small-en-v1.5 bun run src/indexer.ts ./docs
2. Search Documents
# Basic search
bun run src/query.ts --run "What is the FuelVM?"
# Custom number of results
NUM_RESULTS=10 bun run src/query.ts --run "smart contracts"
3. Run MCP Server
# Start MCP server (stdio-mode)
bun run src/cli.ts
# With HTTP transport
bun run src/cli.ts --transport http --port 3500
4. Run Tests
bun test
Environment Variables
| Variable | Default | Description |
|---|---|---|
VECTRA_INDEX_PATH | ./vectra_index | Vector database location |
EMBEDDING_MODEL | Xenova/all-MiniLM-L6-v2 | Hugging Face model |
CHUNK_SIZE | 2000 | Target tokens per chunk |
NUM_RESULTS | 5 | Search results count |
LOG_LEVEL | Set to debug for verbose output |
Implementation Details
- Chunking: Preserves code blocks, splits by paragraphs with context awareness
- Indexing: Generates embeddings with enhanced metadata for better search
- Querying: Semantic search with quality filtering and keyword fallback
- MCP Server: Exposes search as tool via stdio communication
- Storage: File-based Vectra index (no external database required)
API
MCP Tools
searchFuelDocs
query(string): Search querynResults(number, optional): Number of results (default: 5)includeScore(boolean, optional): Include relevance scores
provideStdContext
- Returns Sway standard library paths and types
Development
# Install dependencies
bun install
# Run tests
bun test
# Index sample docs
bun run src/indexer.ts ./docs
# Test search functionality
bun run src/query.ts --run "test query"
# Start MCP server for development (STDIO)
bun run src/cli.ts
# Start MCP server for development (HTTP)
bun run src/cli.ts --transport http --port 3500
เซิร์ฟเวอร์ที่เกี่ยวข้อง
HeadHunter
An MCP server for the HeadHunter API, focusing on job seeker functionalities.
Spryker Search Tool
Search Spryker packages, documentation, and code within Spryker GitHub repositories using natural language.
AgentRank
Google for AI agents — live search across 25,000+ scored MCP servers, updated daily
Singapore Location Intelligence MCP
Provides real-time Singapore transport data and routing information.
PulseMCP Server
Discover and explore MCP servers and integrations using the PulseMCP API.
Legal MCP Server
Court records, patent search, trademark lookup, and legal document research
Library Docs MCP Server
Search and fetch documentation for popular libraries like Langchain, Llama-Index, and OpenAI using the Serper API, providing updated information for LLMs.
Fish MCP Server
Search for fish species using the FishBase database. Supports natural language queries in both Japanese and English.
Qdrant RAG MCP Server
A semantic search server for codebases using Qdrant, featuring intelligent GitHub issue and project management.
Rijksmuseum MCP Server
Explore the Rijksmuseum's art collection using natural language.