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
関連サーバー
ReActMCP Web Search
A web search server that integrates with the Exa API to perform basic and advanced searches.
Scientific Paper Harvester
Harvests scientific papers from arXiv and OpenAlex, providing real-time access to metadata and full text.
Parquet MCP Server
An MCP server for web and similarity search, designed for Claude Desktop. It integrates with various external embedding and API services.
Higress AI-Search MCP Server
Provides an AI search tool to enhance AI model responses with real-time search results from various search engines using the Higress ai-search feature.
OpenStreetMap
Enhances LLMs with location-based services and geospatial data from OpenStreetMap.
DNDzgz
Get real-time public transport information for Zaragoza using the DNDzgz API.
PubTator MCP Server
A server for biomedical literature annotation and relationship mining, based on PubTator3.
Perplexity MCP Server
Perform real-time internet research with source citations using the Perplexity API.
BrowseAI Dev
Evidence-backed web research for AI agents. BM25+NLI claim verification, confidence scores, citations, contradiction detection. 12 MCP tools. Works with Claude Desktop, Cursor, Windsurf. Python SDK (pip install browseaidev), LangChain, CrewAI, LlamaIndex integrations. npx browseai-dev
Brave Search
An MCP server for the Brave Search API, providing web and local search capabilities via a streaming SSE interface.