What platforms does Official Substack / Medium MCP Server support?
Substack and Medium are supported via their RSS feeds.
@jean-technologies
基本情報
The tool connects to your Substack/Medium blogs via their RSS feeds, fetches your posts, and permanently caches them locally. It also generates embeddings for each post, enabling semantic search to find the most relevant essays based on your queries.
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": {
"writer-tool": {
"command": "/absolute/path/to/uv",
"args": [
"--directory",
"/absolute/path/to/writer-context-tool",
"run",
"writer_tool.py"
]
}
}
}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。
Official Substack / Medium MCP Server is an open-source Model Context Protocol (MCP) implementation that connects Claude to your Substack and Medium writing. It allows Claude to access, cache, and semantically search your published blog posts, providing personalized writing assistance.
Clone the repository, set up a Python 3.10+ environment with uv or pip, and configure your Substack/Medium RSS URLs in a config.json file. Then connect to Claude Desktop by adding the server to the claude_desktop_config.json configuration. Once running, Claude can access individual essay resources and use the search_writing and refresh_content tools.
search_writing and refresh_contentSubstack and Medium are supported via their RSS feeds.
Content is stored on disk and persists between sessions. The cache duration is configurable (default 10080 minutes / 1 week). Content and embeddings are automatically refreshed at startup and on demand using the `refresh_content` tool.
Python 3.10 or higher, the latest Claude Desktop, and a Substack or Medium account with published public content.
Check that your Claude Desktop configuration file has correct absolute paths, verify all packages are installed, and restart Claude Desktop. You can also try the shell script alternative for the uv command.
Each essay is converted into embeddings at startup. When you use the `search_writing` tool, embeddings are compared to find the most relevant essays, with the number of results configurable via `similar_posts_count`.
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