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.
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.pyfile. - 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/sdkto 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
piporuv. - 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.
-
Clone the repository:
git clone https://github.com/your-repo/notemd-mcp.git cd notemd-mcp -
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
- Using
-
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/docsin 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 itsname,apiKey,baseUrl,model,temperature, and optionalapiVersion(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 withinVAULT_ROOTwhere 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, ifSEARCH_PROVIDERis 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 theprocess_content(add links) operation.RESEARCH_PROVIDER: The LLM provider to use for theresearch_summarizeoperation.GENERATE_TITLE_PROVIDER: The LLM provider to use for thegenerate_titleoperation.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 theprocess_content(add links) operation.CUSTOM_PROMPT_GENERATE_TITLE: Custom prompt string for thegenerate_titleoperation.CUSTOM_PROMPT_RESEARCH_SUMMARIZE: Custom prompt string for theresearch_summarizeoperation.
License
This project is licensed under the MIT License. See the LICENSE file for details.