Model Context Protocol server for storytelling - AI-powered narrative generation
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# Storytelling MCP Server Model Context Protocol (MCP) integration for the Storytelling package. Exposes AI-powered narrative generation as composable MCP tools and resources. ## Overview The Storytelling MCP Server enables LLM agents and CLI assistants (like Claude, ChatGPT, or GitHub Copilot) to: - **Generate complete stories** from prompts using advanced orchestration - **Manage sessions** - list, retrieve info, resume, migrate interrupted generations - **Configure & validate** - comprehensive configuration reference and validation - **Setup knowledge bases** - RAG-aware story generation with your documents - **Choose models wisely** - provider listing and model recommendations - **Access workflow resources** - understand each stage of story generation - **Explore advanced features** - translation, IAIP, content checking, debugging ## Installation ### As MCP Server ```bash # Install storytelling-mcp from PyPI pip install storytelling-mcp # Or with all storytelling features pip install storytelling[all] ``` ### Configure for Claude Desktop Add to `~/.config/claude_app/claude_desktop_config.json`: ```json { "mcpServers": { "storytelling": { "command": "python", "args": ["-m", "storytelling_mcp"] } } } ``` ### Configure for GitHub Copilot ```bash # Make the server available as a tool export MCP_STORYTELLING=python://storytelling_mcp.server ``` ## Available Tools (16 Total) ### Story Generation (1 tool) #### `generate_story` Generate a complete, multi-chapter story from a prompt. **Parameters:** - `prompt_file` (required): Path to prompt file containing story concept - `output_file` (optional): Where to save the generated story (auto-generated if omitted) - `initial_outline_model`: LLM for initial outline (default: `google://gemini-2.5-flash`) - `chapter_outline_model`: LLM for chapter planning - `chapter_s1_model` through `chapter_s4_model`: LLMs for individual scenes - `chapter_revision_model`: LLM for chapter revision - `…
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