Factors.ai MCP server — agent management and execution tools
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# factors-mcp Python package exposing a **Model Context Protocol (MCP)** server for Factors.ai — list/create/update agents, run agents against domains, and use bundled prompt guidelines. ## Requirements - Python 3.10+ - A Factors **private API token** (`FACTORS_API_KEY`) with access to the Factors API (`FACTORS_API_URL`) ## Environment variables | Variable | Default | Purpose | | --- | --- | --- | | `FACTORS_API_KEY` | _(required)_ | Private API token for Factors API calls | | `FACTORS_API_URL` | `https://api.factors.ai` | Factors API base URL | | `FACTORS_HTTP_TIMEOUT_SECONDS` | `30` | HTTP timeout (seconds) for all tool calls except `run_agent` | `run_agent` always uses a **300s** HTTP timeout because agent runs can take several minutes. ## Client configuration (Claude Desktop & Cursor) Both clients drive the server over **stdio**. Point them at the Python interpreter or console script from the **same virtualenv** where you installed `factors-mcp`, and pass your API credentials in **`env`** (do not commit real keys into shared project files). ### Claude Desktop 1. Quit Claude Desktop completely. 2. Open the config file in a text editor: - **macOS:** `~/Library/Application Support/Claude/claude_desktop_config.json` - **Windows:** `%APPDATA%\Claude\claude_desktop_config.json` 3. Merge the `mcpServers` entry below with any existing `mcpServers` object (do not remove other servers). ```json { "mcpServers": { "factors-ai": { "command": "python3", "args": ["-m", "factors-mcp"], "env": { "FACTORS_API_KEY": "your_private_token" } } } } ``` Restart Claude Desktop after saving. ### Cursor 1. Create or edit an MCP config file (JSON): - **Project:** `<repo-root>/.cursor/mcp.json` (optional; share with the team without secrets) - **User-wide:** `~/.cursor/mcp.json` (macOS/Linux) or `%USERPROFILE%\.cursor\mcp.json` (Windows) 2. Add a `mcpServers` entry (merge with existing servers if the file already exists)…
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