Fuel Network & Sway Language MCP Server
@FuelLabs
About Fuel Network & Sway Language MCP Server
A Fuel MCP server which provides support for Fuel docs and various coding IDEs such as Cursor.
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"fuel-mcp-server": {
"command": "bun",
"args": [
"run",
"src/indexer.ts",
"./docs"
]
}
}
}Tools
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Overview
What is Fuel Network & Sway Language MCP Server?
It is a Model Context Protocol server that indexes Fuel Network and Sway Language documentation into a local vector database, enabling IDE integration (e.g., Cursor) for semantic search of docs.fuel.network content directly within the development environment.
How to use Fuel Network & Sway Language MCP Server?
Clone the repository, install dependencies with bun install, index documentation with bun run src/indexer.ts, then start the MCP server with bun run src/cli.ts. Add its configuration to your MCP client (e.g., Claude or Cursor) by specifying the command bun run <absolute_path>/src/cli.ts --transport stdio.
Key features of Fuel Network & Sway Language MCP Server
- Local semantic search using open‑source embeddings (Transformers.js)
- No Docker required – runs solely with Bun
- Fast file‑based vector storage using Vectra
- Hybrid search with keyword fallback for robustness
- Enhanced result filtering and formatting
- Supports both STDIO and HTTP transports
Use cases of Fuel Network & Sway Language MCP Server
- Searching FuelVM documentation without leaving an IDE
- Retrieving Sway standard library paths and types via MCP tools
- Indexing and querying local Markdown documentation offline
- Integrating contextual documentation lookup into AI‑assisted coding workflows
- Developing and testing Fuel‑based smart contracts with instant doc access
FAQ from Fuel Network & Sway Language MCP Server
How does it differ from online documentation search?
Unlike web‑based searches, the server indexes docs locally, works offline, and integrates directly into MCP‑compatible IDEs for faster, contextual results.
What are the runtime dependencies?
Only Bun is required. No Docker, Python, or external embedding services are needed.
Where is the documentation data stored?
Document embeddings are stored in a local vectra_index directory created after running the indexer. The path can be customized via the VECTRA_INDEX_PATH environment variable.
What transport protocols does it support?
STDIO (default) and HTTP transport (e.g., --transport http --port 3500). Health checks are available at /health.
Are there any known limits?
The server is designed for local use; it does not connect to external APIs. The search quality depends on the indexed documents and the chosen embedding model.
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