Working with MCP Servers on LangDB

In the evolving landscape of AI-powered applications, integrating multiple sources of data efficiently is key to building robust and intelligent systems. Model Context Protocol (MCP) provides a standardized framework that enables AI models to connect to various external services seamlessly while maintaining flexibility, security, and scalability.

LangDB makes it incredibly simple to work with MCP servers by enabling OpenAI-compatible API access to different LLMs and external search providers with minimal configuration.

What is MCP?

MCP (Model Context Protocol) provides a framework for AI models to connect to multiple external services through a standardized protocol. With native tool integrations, MCP connects AI models to APIs, databases, local files, automation tools, and remote services. This allows developers to effortlessly integrate MCP with IDEs, business workflows, and cloud platforms, while retaining the flexibility to switch between LLM providers. This enables the creation of intelligent, multi-modal workflows where AI securely interacts with real-world data and tools.

Where is MCP Used?

MCP is widely used across various fields to enhance AI functionality:

• Data and Storage: Enables structured data management and retrieval.

• Cloud & Infrastructure: Supports distributed computing and secure content delivery.

• Development Tools: Automates repository management and error tracking.

• Content and Search: Enhances search, indexing, and geolocation services.

• AI & Memory: Powers vector search and machine learning applications.

• Productivity: Integrates AI into collaboration and document management tools.

• System & Utilities: Improves system automation and workflow efficiency.

LangDB simplifies MCP integration by sending requests to the appropriate MCP server based on the LLM's response, ensuring seamless retrieval of relevant data while enabling full tracing of interactions. This allows developers to monitor requests, analyze performance, and optimize responses efficiently.

Using MCP with LangDB

By specifying an MCP server in the request, developers can ensure seamless interactions between AI models and external data sources.

import os
from openai import OpenAI
client = OpenAI(
    api_key=os.getenv("LANGDB_API_KEY"),
    base_url=os.getenv("LANGDB_API_URL")
)
extra_body = {
    "mcp_servers": [
        {
            "server_url": "wss://your-mcp-server.com/ws?config=your_encoded_config",
            "type": "ws"
        }
    ]
}
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "what is langdb?"}],
    extra_body=extra_body
)

1. EXA Search Integration

EXA Search is a powerful search engine that provides structured and contextualized search results. Using LangDB, we can integrate EXA Search with minimal configuration.

2. Github

GitHub MCP Server enables seamless access to the GitHub API, allowing developers to perform file operations, repository management, search functionality, and more.

MCP Tracing and Observability in LangDB

One of the key benefits of using LangDB for MCP integration is full tracing and observability. With a simple change, developers can track requests, optimize response times, and analyze usage across different providers.

LangDB automatically manages these integrations, ensuring seamless transitions between different MCP servers without additional infrastructure overhead.

Using Smithery for MCP Deployments

We leveraged Smithery to streamline MCP deployments. To learn more:

• Visit the LangDB Samples Repository for setup instructions and examples.

• Checkout Smithery in the documentation.

For more details, visit the Model Context Protocol official page and explore Anthropic MCP documentation.