What prerequisites are needed to run the server?
Python 3.10 or higher, a Typefully account with API access, and a Typefully API key (available from Settings > Integrations in Typefully).
@pepuscz
Basic information
MCP server for Typefully service
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"typefully": {
"command": "/path/to/your/typefully-mcp-server/venv/bin/python",
"args": [
"-m",
"typefully_mcp_server.server"
],
"cwd": "/path/to/your/typefully-mcp-server",
"env": {
"TYPEFULLY_API_KEY": "your_actual_api_key_here"
}
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
A Model Context Protocol (MCP) server that integrates with the Typefully API, allowing AI assistants to create and manage drafts on Typefully. It is intended for developers who use MCP-compatible AI tools like Cursor or Claude Desktop.
Install from source with Python 3.10+, configure your Typefully API key via macOS Keychain or an environment variable, then add the MCP server configuration to your MCP client. Once set up, invoke tools such as create_draft, get_scheduled_drafts, and get_published_drafts through natural language commands.
Python 3.10 or higher, a Typefully account with API access, and a Typefully API key (available from Settings > Integrations in Typefully).
You can store it in the macOS Keychain (Service: `typefully-mcp-server`, Account: `api_key`) or set it as an environment variable. Environment variables take priority over keychain storage.
The server provides `create_draft`, `get_scheduled_drafts`, and `get_published_drafts`. Each tool accepts optional parameters for content filtering, scheduling, and threading.
Yes. Both `get_scheduled_drafts` and `get_published_drafts` accept a `content_filter` parameter that accepts `"tweets"` or `"threads"` to narrow results.
Run the included `test_read_api.py` script after configuring your API key and activating the virtual environment. It verifies API connectivity.

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