Langgraph-MCP Client for Postgresql Example
LocalAbout Langgraph-MCP Client for Postgresql Example
This example shows a LangGraph query agent using MCP tools from a PostgreSQL-connected MCP server. It is intended for exploring a basic LangGraph and MCP integration flow.
Key features
- 01LangGraph query-agent example
- 02Load MCP tools
- 03Connect to a PostgreSQL MCP server
- 04Stream output to a file
Use cases
- 01Build natural-language queries over a database
- 02Explore how LangGraph invokes MCP tools
MCP support
The example loads tools from a PostgreSQL MCP server with `load_mcp_tools` for use by a LangGraph agent.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMELanggraph-MCP Client for Postgresql Example
This repository demonstrates a very low level example of how to interact with an MCP server as a docker image, connected to a PostgreSQL database for LangGraph agents.
MCP is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.
Prerequisites
Before you begin, ensure you have the following installed on your system:
- Docker
- PostgreSQL Database - running and populated
- MCP Servers
Flow and tools
- We use a postgresql database that the Postgresl MCP Server is aware of through the config
from langchain_mcp_adapters.tools import load_mcp_toolscreates the mcp toolcreate_react_agentis a fast agent templater- we output the stream to file
Project Setup Instructions
1. Clone the Repository
Clone this repository to your local machine:
git clone https://github.com/your-repo/Langchain-MCP.git
cd Langgraph-mcp-client directory
2. Create a .env File
Create a .env file in the root of the repository with the following variables:
DB_HOST=<your-database-host>
DB_PORT=<your-database-port>
DB_USER=<your-database-username>
DB_PASSWORD=<your-database-password>
DB_NAME=<your-database-name>
Replace the placeholders with your actual database and MCP server connection details.
3. Install UV Package
Install the UV package globally if you haven't already:
MCP Server Setup
- Clone the repo MCP Servers to a separate directory
- Run -
docker build -t mcp/postgres -f src/postgres/Dockerfile .to build and tag the image
Usage Instructions
1. Sync the UV Package
Run the following command to sync the UV package:
uv sync
This command ensures that the UV package installs the package correctly.
2. Run the Query Agent
Start the query agent using the following command:
uv run queryagent
This will prompt for the user query
Alt - with query (Banking database)
uv run queryagent "Who holds the most funds in thier account?"
TODO:
- Output formating
- Multi agent
Troubleshooting
- Database Connection: Verify the
.envfile contains the correct database credentials. - UV Package Errors: Ensure the UV CLI is installed globally and the
uv synccommand completes without errors.
Contributing
Feel free to open issues or submit pull requests to improve this repository.
License
This project is licensed under the MIT License. See the LICENSE file for details.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.