# 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](https://github.com/Jacobinwwey/obsidian-NotEMD).
[English](./README.md) | [简体中文](./README_zh.md)
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.
```bash
# 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:**
```bash
git clone https://github.com/your-repo/notemd-mcp.git
cd notemd-mcp
```
2. **Install dependencies:**
* **Using `uv` (Recommended):**
```bash
uv venv
uv pip install -r requirements.txt
```
* **Using `pip`:**
```bash
python -m venv .venv
# Activate the environment (e.g., source .venv/bin/activate)
pip install -r requirements.txt
```
3. **Run the server:**
```bash
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:
```json
{
"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.
