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Fuel Network & Sway Language MCP Server

@FuelLabs

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その他

トランスポート

stdio

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FuelLabs

Fuel Network & Sway Language MCP Server について

A Fuel MCP server which provides support for Fuel docs and various coding IDEs such as Cursor.

Fuel Network & Sway Language MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "fuel-mcp-server": {
      "command": "bun",
      "args": [
        "run",
        "src/indexer.ts",
        "./docs"
      ]
    }
  }
}

Fuel Network & Sway Language MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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.

2

What are the runtime dependencies?

Only Bun is required. No Docker, Python, or external embedding services are needed.

3

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.

4

What transport protocols does it support?

STDIO (default) and HTTP transport (e.g., `--transport http --port 3500`). Health checks are available at `/health`.

5

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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