What are the prerequisites for running this project?
You need Docker, a running PostgreSQL database, and the MCP Servers repository (to build the Postgres MCP image). The UV package manager must also be installed globally.
@BrianCusack
Basic information
LangGraph implementation interacting with a PostgreSQL MCP Server
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"LangGraph-MCP-Client": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tool list not available yet
This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.
We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.
This repository demonstrates a low-level example of how to interact with an MCP server as a Docker image, connected to a PostgreSQL database for LangGraph agents. It is intended for developers who want to integrate PostgreSQL data sources with LangGraph using the Model Context Protocol (MCP).
Clone the repository, create a .env file with database credentials (DB_HOST, DB_PORT, DB_USER, DB_PASSWORD, DB_NAME), install the UV package globally, and build the MCP Postgres Docker image. Then run uv sync followed by uv run queryagent to start the query agent, optionally passing a natural language query as an argument (e.g., uv run queryagent "Who holds the most funds in their account?").
langchain_mcp_adapters.tools.load_mcp_tools to create MCP tools.create_react_agent for fast agent templating.You need Docker, a running PostgreSQL database, and the MCP Servers repository (to build the Postgres MCP image). The UV package manager must also be installed globally.
Create a `.env` file in the repository root with the variables `DB_HOST`, `DB_PORT`, `DB_USER`, `DB_PASSWORD`, and `DB_NAME`, replacing placeholders with your actual credentials.
Ensure the UV CLI is installed globally and that the `uv sync` command completes without errors.
Clone the MCP Servers repository, navigate to its directory, and run `docker build -t mcp/postgres -f src/postgres/Dockerfile .`
Run `uv run queryagent "your question"` from the project directory, replacing `"your question"` with the desired natural language query.
This is a very basic implementation of an Mcp-Reasoning-Server for Cursor AI .
This a simple implementation of an MCP server using iFlytek. It enables calling iFlytek workflows through MCP tools.
AI-powered n8n workflow automation through natural language. MCP server enabling Claude AI & Cursor IDE to create, manage, and monitor workflows via Model Context Protocol. Multi-instance support, 17 tools, comprehensive docs. Build workflows conversationally without manual JSON
This repository contains hands-on projects, code examples, and deployment workflows. Explore multi-agent systems, LangChain, LangGraph, AutoGen, CrewAI, RAG, MCP, automation with n8n, and scalable agent deployment using Docker, AWS, and BentoML.
An MCP (Model Context Protocol) server that helps AI assistants (like Claude) break down complex tasks into manageable steps, track progress, and manage a hierarchical task list.
Comments