MCP Registry

# MCP Server ### 🧩 Main modules 1. **Main MCP server** - the core of the system, performing the following functions: - Registry of connected servers - Routing requests between servers - Monitoring server status - Aggregation of information about available tools 2. **Specialized servers** (connect to the main one): - **Embedding server** - working with vector representations of text - **PDF extract server** - conversion and extraction from PDF to Markdown - **Reranker server** - ranking text data - **Qdrant server** - managing vector collections - **PostgreSQL server** - executing SQL queries and schema inspection - **LLM server** - generating and streaming LLM responses, list of models - **MarkUp server** - text/file markup using markup service methods - **Transcribe server** - audio loading, status and transcription result ### ⚙️ Available tools | Server | Methods | |---|---| | **Embedding server** | `embedding_generate`, `embedding_batch_generate`, `embedding_get_models`, `health_check` | | **PDF extract server** | `document_convert_to_markdown`, `document_get_supported_formats`, `health_check` | | **Reranker server** | `rerank_documents`, `health_check` | | **Qdrant server** | `vector_create_collection`, `vector_get_collection_info`, `vector_upsert_points`, `vector_search`, `vector_delete_points`, `health_check` | | **PostgreSQL server** | `postgres_execute_query`, `postgres_get_schema`, `postgres_create_table`, `postgres_insert_data`, `health_check` | | **LLM server** | `llm_chat_completion`, `llm_get_models`, `llm_stream_completion`, `health_check` | | **MarkUp server** | `markup_get_methods`, `markup_process_text`, `markup_process_file`, `health_check` | | **Transcribe server** | `transcribe_audio`, `transcribe_get_status`, `transcribe_get_result`, `health_check` | ##### Note: To add a new FastMCP server, you need to import it in the `main_server.py` file and place it in the `MCP_SERVERS` array, after which the main methods of `main_server.py` will have access to it. Also a necessary requirement for FastMCP servers is the presence of the `health_check` method to check the state. ### 📡 Main server methods ```python get_server_and_tools() # Get a list of all servers and tools router(server_name, tool_name, params) # Routing requests health_check_servers() # Checking the health of all servers ``` ### Setting up the environment Create a `.env` file in the root of the project and put the following environment variables in it (the list corresponds to the use in the code): ``` # Main server MAIN_SERVER_API_KEY=... # Embedding server EMBEDDING_API_KEY=... EMBEDDING_URL=... EMBEDDING_MODEL_NAME=... EMBEDDING_URL_MODELS=... EMBEDDING_HEALTH_URL=... # PDF extract server PDF_EXTRACTOR_URL=... PDF_HEALTH_URL=... # Reranker server RERANK_URL=... RERANK_MODEL=... RERANK_HEALTH_URL=... # Qdrant server QDRANT_URL=... QDRANT_API_KEY=... QDRANT_HEALTH_CHECK_URL=... # PostgreSQL server POSTGRES_USER=... POSTGRES_PASSWORD=... POSTGRES_HOST=... POSTGRES_DB=... #LLM server LLM_SERVICE_API_KEY=... LLM_SERVICE_MODEL=... LLM_SERVICE_CHAT_COMPLETIONS_URL=... LLM_SERVICE_MODELS_URL=... LLM_SERVICE_COMPLETIONS_URL=... LLM_SERVICE_HEALTH_URL=... # MarkUp server MARKUP_API_KEY=... MARKUP_GET_METHODS_URL=... MARKUP_PROCESS_TEXT_URL=... MARKUP_PROCESS_FILE_URL=... MARKUP_HEALTH_CHECK_URL=... # Transcribe server TRANSCRIBE_API_KEY=... TRANSCRIBE_UPLOAD_AUDIO=... TRANSCRIBE_HEALTH_URL=... ``` # Start the main server ### Installation dependencies and creating a virtual environment Before starting the server, it is recommended to create a virtual environment and install all dependencies from `requirements.txt`. Run the following commands in the terminal: 1. Creating a virtual environment ```bash python -m venv venv ``` 2. Activating the environment ```bash ./venv/Scripts/activate ``` 3. Installing dependencies ```bash pip install -r requirements.txt ``` After setting up the environment, the server is started with the command ```bash fastmcp run ./main_server.py:main_mcp_server --transport http ``` ### Running in Docker 1. Build the image: ```bash docker build -t mcp-main-server . ``` 2. Run the container, passing `.env` as environment variables: ```bash docker run --rm -p 8000:8000 --env-file .env mcp-main-server ``` ### Configuring the server connection in Cursor 0. Run the server using the command above 1. Open the settings 2. Add the MCP server configuration: ```json { "mcpServers": { "main-registry": { "url": "http://localhost:8000/mcp/" }}} ``` # Local MCP server: proxy_mcp_server - **What is it**: proxy MCP server that connects to the main registry (`main_server`) and forwards its methods, and provides a high-level pipeline for pre-preparing data for RAG. - **Where is it**: `proxy_mcp_server/proxy_mcp_server.py` - **Available tools**: - `get_server_and_tools()` — get a list of servers and their tools from the registry - `router(server_name: str, tool_name: str, params: dict)` — universal call router - `preprocessing_data_for_rag(file_paths: List[str]) -> str` — prepare PDF/texts and create a collection in Qdrant; returns the collection name - `health_check_servers()` — check if all services are available Requirements: - `main_server` is running and accessible via URL (e.g. `http://localhost:8000/mcp/`). - Valid API key `MAIN_SERVER_API_KEY` (must match `Authorization` header in `proxy_mcp_server.py`). - Update `url` and `headers.Authorization` in `config` object inside `proxy_mcp_server/proxy_mcp_server.py` if necessary. Connection in Cursor (example): ```json { "mcpServers": { "proxy-server": { "command": "uv", "args": [ "run", "fastmcp", "run", "YOUR_PATH_TO/proxy_mcp_server/proxy_mcp_server.py:proxy_mcp_server"] }}} ``` Launch from terminal: ``` fastmcp run ./proxy_mcp_server/proxy_mcp_server.py:proxy_mcp_server ``` # RAG inference: interactive launch - **What is this**: console assistant for asking questions to a collection of documents in Qdrant with additional ranking and generation of LLM response. - **Location**: `rag_inference/RAG workflow.py` - **Preliminary environment variables**: `QDRANT_URL`, `QDRANT_API_KEY`, `RERANK_URL`, `RERANK_MODEL`, `LLM_SERVICE_CHAT_COMPLETIONS_URL`, `LLM_SERVICE_API_KEY`, `LLM_SERVICE_MODEL`, `EMBEDDING_URL`, `EMBEDDING_MODEL` are used (described above in the settings section). Run (Windows PowerShell): ``` python ".\rag_inference\RAG workflow.py" <collection_name> ``` Where `<collection_name>` is the name of the collection in Qdrant. It is convenient to get it in advance by calling the `preprocessing_data_for_rag` tool from `proxy_mcp_server` and passing a list of files to index; the method will return the name of the created collection. Example: ``` python ".\rag_inference\RAG workflow.py" collection_for_rag_1 ```