Notemd MCP (Mission Control Platform) Server

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   AI-Powered Backend for Your Knowledge Base
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Welcome to the Notemd MCP Server! This project provides a powerful, standalone backend server that exposes the core AI-powered text processing and knowledge management functionalities of the Notemd Obsidian Plugin.

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Built with Python and FastAPI, this server allows you to offload heavy computational tasks from the client and provides a robust API to interact with your knowledge base programmatically.

Features

• AI-Powered Content Enrichment: Automatically processes Markdown content to identify key concepts and create [[wiki-links]], building a deeply interconnected knowledge graph.
• Automated Documentation Generation: Generates comprehensive, structured documentation from a single title or keyword, optionally using web research for context.
• Integrated Web Research & Summarization: Performs web searches using Tavily or DuckDuckGo and uses an LLM to provide concise summaries on any topic.
• Knowledge Graph Integrity: Includes endpoints to automatically update or remove backlinks when files are renamed or deleted, preventing broken links.
• Syntax Correction: Provides a utility to batch-fix common Mermaid.js and LaTeX syntax errors often found in LLM-generated content.
• Highly Configurable: All major features, API keys, file paths, and model parameters are easily managed in a central config.py file.
• Multi-LLM Support: Compatible with any OpenAI-compliant API, including local models via LMStudio and Ollama, and cloud providers like DeepSeek, Anthropic, Google, and more.
• Interactive API Docs: Comes with automatically generated, interactive API documentation via Swagger UI.

How It Works

The server is built on a simple and logical architecture:

• main.py (API Layer): Defines all API endpoints using the FastAPI framework. It handles incoming requests, validates data using Pydantic, and calls the appropriate functions from the core logic layer.
• notemd_core.py (Logic Layer): The engine of the application. It contains all the business logic for interacting with LLMs, processing text, performing web searches, and managing files within your knowledge base.
• config.py (User-Defined Space): The central configuration hub. This is where you define your file paths, API keys, and tune the behavior of the server to fit your needs.
• cli.js (MCP Bridge): A Node.js-based command-line interface that acts as a bridge to the Python server. It uses the @modelcontextprotocol/sdk to create a server that can be called by other tools. It starts the FastAPI server and then communicates with it via HTTP requests.

Getting Started

Follow these steps to get the Notemd MCP server up and running on your local machine.

Prerequisites

• For Python execution: Python 3.8+ and pip or uv.
• For NPX execution: Node.js and npx.

Installation & Running

Choose the method that best fits your workflow.

Method 1: Using npx (Recommended for Quick Start)

This is the simplest way to start the server. npx will temporarily download and run the package. This method now supports stdio mode, meaning you will see the FastAPI server logs directly in your terminal.

# This single command will download the package and start the server.
npx notemd-mcp-server

Method 2: Local Installation with uv or pip

This method is for users who want to clone the repository and manage the files locally.

1. Clone the repository:

   git clone https://github.com/your-repo/notemd-mcp.git
   cd notemd-mcp
   

2. Install dependencies:

   • Using uv (Recommended):

     uv venv
     uv pip install -r requirements.txt
     

   • Using pip:

     python -m venv .venv
     # Activate the environment (e.g., source .venv/bin/activate)
     pip install -r requirements.txt
     

3. Run the server:

   uvicorn main:app --reload
   

Method 3: MCP Configuration

To integrate Notemd MCP with your Mission Control Platform (MCP) setup, add the following to the mcpServers object in your settings.json file:

 {
   "mcpServers": {
     "notemd-mcp": {
       "description": "Notemd MCP Server - AI-powered text processing and knowledge management
 for your Markdown files.",
       "command": "npx",
       "args": [
         "-y",
         "notemd-mcp-server"
       ],
       "env": {
         "OPENAI_API_KEY": "your_openai_api_key_here",
         "DEEPSEEK_API_KEY": "your_deepseek_api_key_here"
       }
     }
   }
 }

Usage

The best way to explore and interact with the API is through the automatically generated documentation.

• Navigate to http://127.0.0.1:8000/docs in your web browser.

You will see a complete, interactive Swagger UI where you can view details for each endpoint, see request models, and even send test requests directly from your browser.

API Endpoints

Endpoint	Method	Description	Request Body	Response
/process_content	POST	Takes a block of text and enriches it with [[wiki-links]].	{"content": "string", "cancelled": "boolean"}	{"processed_content": "string"}
/generate_title	POST	Generates full documentation from a single title.	{"title": "string", "cancelled": "boolean"}	{"generated_content": "string"}
/research_summarize	POST	Performs a web search on a topic and returns an AI-generated summary.	{"topic": "string", "cancelled": "boolean"}	{"summary": "string"}
/execute_custom_prompt	POST	Execute a user-defined prompt with given content.	{"prompt": "string", "content": "string", "cancelled": "boolean"}	{"response": "string"}
/handle_file_rename	POST	Updates all backlinks in the vault when a file is renamed.	{"old_path": "string", "new_path": "string"}	{"status": "success"}
/handle_file_delete	POST	Removes all backlinks to a file that has been deleted.	{"path": "string"}	{"status": "success"}
/batch_fix_mermaid	POST	Scans a folder and corrects common Mermaid.js and LaTeX syntax errors in .md files.	{"folder_path": "string"}	{"errors": [], "modified_count": "integer"}
/health	GET	A simple health check to confirm the server is running.	(None)	{"status": "ok"}

Configuration

All configuration is handled in the config.py file. Here you can set API keys, file paths, and other settings.

Core Settings

The notemd_core.set_settings function in main.py initializes the core functionalities of the server using the following parameters, primarily sourced from config.py:

• DEFAULT_PROVIDERS: A list of dictionaries, each defining an LLM provider with its name, apiKey, baseUrl, model, temperature, and optional apiVersion (for Azure OpenAI).
• ACTIVE_PROVIDER: The name of the LLM provider to be used by default for all operations.
• CHUNK_WORD_COUNT: The maximum number of words per chunk when processing content for wiki-linking.
• MAX_TOKENS: The maximum number of tokens allowed for LLM interactions.
• ENABLE_DUPLICATE_DETECTION: Boolean to enable/disable duplicate concept detection during wiki-linking.

File Paths Configuration

These settings define the directory structure for your knowledge base and logs:

• VAULT_ROOT: The absolute path to your Obsidian vault or the root directory of your Markdown files.
• CONCEPT_NOTE_FOLDER: The subfolder within VAULT_ROOT where generated concept notes will be stored.
• PROCESSED_FILE_FOLDER: The subfolder where processed Markdown files will be moved.
• CONCEPT_LOG_FOLDER: The subfolder for storing concept generation logs.
• CONCEPT_LOG_FILE_NAME: The name of the log file for concept generation.

Search Configuration

Settings related to web research and summarization:

• TAVILY_API_KEY: Your API key for Tavily, if SEARCH_PROVIDER is set to "tavily".
• SEARCH_PROVIDER: Specifies the web search engine to use ("tavily" or "duckduckgo").
• DDG_MAX_RESULTS: Maximum number of results to fetch from DuckDuckGo.
• DDG_FETCH_TIMEOUT: Timeout in seconds for DuckDuckGo searches.
• MAX_RESEARCH_CONTENT_TOKENS: Maximum tokens for content used in research.
• ENABLE_RESEARCH_IN_GENERATE_CONTENT: Boolean to enable/disable web research when generating content from a title.
• TAVILY_MAX_RESULTS: Maximum number of results to fetch from Tavily.
• TAVILY_SEARCH_DEPTH: Search depth for Tavily ("basic" or "advanced").

Stable API Call Settings

These settings control the retry mechanism for LLM API calls:

• ENABLE_STABLE_API_CALL: Boolean to enable/disable stable API calls with retries.
• API_CALL_INTERVAL: Interval in seconds between API call retries.
• API_CALL_MAX_RETRIES: Maximum number of retries for a failed API call.

Multi-Model and Task-Specific Settings

These settings allow for fine-grained control over which LLM provider and model are used for specific tasks:

• ADD_LINKS_PROVIDER: The LLM provider to use for the process_content (add links) operation.
• RESEARCH_PROVIDER: The LLM provider to use for the research_summarize operation.
• GENERATE_TITLE_PROVIDER: The LLM provider to use for the generate_title operation.
• ADD_LINKS_MODEL: Specific model to use for adding links (overrides provider's default if set).
• RESEARCH_MODEL: Specific model to use for research (overrides provider's default if set).
• GENERATE_TITLE_MODEL: Specific model to use for title generation (overrides provider's default if set).

Post-processing Settings

• REMOVE_CODE_FENCES_ON_ADD_LINKS: Boolean to remove code fences from content after adding links.

Language Settings

• LANGUAGE: The default language for content processing.
• AVAILABLE_LANGUAGES: A list of supported languages.

Custom Prompt Settings

These settings allow you to enable and define custom prompts for various operations:

• ENABLE_GLOBAL_CUSTOM_PROMPTS: Boolean to enable/disable the use of custom prompts globally.
• CUSTOM_PROMPT_ADD_LINKS: Custom prompt string for the process_content (add links) operation.
• CUSTOM_PROMPT_GENERATE_TITLE: Custom prompt string for the generate_title operation.
• CUSTOM_PROMPT_RESEARCH_SUMMARIZE: Custom prompt string for the research_summarize operation.

License

This project is licensed under the MIT License. See the LICENSE file for details.