AI model access to Trino with MCP server API for SQL querying and data analysis
ModelContextProtocolServer (MCP Server) is a highly scalable, robust adaptation layer designed to integrate AI applications with diverse data sources and tools through the Model Context Protocol. This protocol ensures seamless communication between AI models like Claude Desktop, Continue, Cursor, and others, providing access to a wide range of backend services without rewriting application logic. By acting as an intermediary, MCP Server enhances the capabilities of these applications by enabling them to interact with external systems in a standardized manner.
The core features of ModelContextProtocolServer are built around the Model Context Protocol (MCP), designed to support AI applications and their integration needs. These include:
ModelContextProtocolServer implements the Model Context Protocol (MCP) by establishing a robust communication channel. This involves:
The protocol flow diagram illustrates this architecture:
graph TD
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server]
C --> D[Data Source/Tool]
style A fill:#e1f5fe
style C fill:#f3e5f5
style D fill:#e8f5e8
MCP Client | Resources | Tools | Prompts | Status |
---|---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ | Full Support |
Continue | ✅ | ✅ | ✅ | Full Support |
Cursor | ❌ | ✅ | ❌ | Tools Only |
To get started, follow these steps to deploy the ModelContextProtocolServer:
git clone https://github.com/stinklabs/modelcontextprotocolserver.git
cd modelcontextprotocolserver
npm install
config.json
with necessary settings:
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
npm start
AI applications frequently require real-time data from various sources to enhance natural language processing tasks. For example, a chatbot could use MCP Server to fetch customer sentiment analysis from social media APIs and incorporate it into its responses.
import requests
# Fetching data using the MCP protocol
response = requests.post("http://localhost:9097/api/query", json={
"query": "SELECT * FROM twitter_sentiment WHERE product='ModelContextProtocol'",
"catalog": "twitter",
"schema": "public"
})
if response.ok:
print(response.json())
else:
print(f"Failed to fetch data: {response.status_code}")
Analytics tools can benefit from dynamic interactions with AI models. For instance, a business analytics dashboard could use MCP Server to retrieve historical sales data and present it in charts, while also allowing users to run custom queries.
// Fetching historic sales data using the MCP protocol
fetch("http://localhost:9081/api/query", {
method: 'POST',
headers: {
'Accept': 'application/json',
'Content-Type': 'application/json'
},
body: JSON.stringify({
query: "SELECT * FROM sales_data WHERE date >= '2023-01-01'",
catalog: "postgres",
schema: "public"
}))
.then(response => response.json())
.then(data => console.log(data))
.catch(error => console.error('Error:', error));
The following are some key points on integrating different MCP clients:
Claude Desktop users can connect directly to various data sources and services using the ModelContextProtocolServer. For example, they could fetch user feedback from a CRM system or analyze market trends through financial APIs.
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Cursor can use the MCP Server to integrate with custom-built plugins or third-party tools. This allows for flexible and extensible workflows where data from diverse sources is aggregated and presented in a unified manner.
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
The performance and compatibility of ModelContextProtocolServer are robust, ensuring smooth operation across a variety of environments:
Advanced configuration options include:
Example security configuration snippet:
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
},
"security": {
"logLevel": "INFO",
"rateLimit": { "requestsPerMin": 100 }
}
}
}
}
ModelContextProtocolServer enforces strict security policies and uses encryption to protect sensitive information. APIs are secured through OAuth2 or JWT tokens, ensuring only authorized clients can access the MCP.
Yes, you can easily add support for new tools if their APIs align with the Model Context Protocol (MCP). Custom configurations can be added to expand client compatibility.
Key challenges include maintaining compatibility across different clients and ensuring high performance under heavy load. Regular updates and rigorous testing help mitigate these issues.
Errors are caught and logged at various levels of granularity to facilitate troubleshooting. Clients receive detailed error messages, enabling them to perform corrective actions.
Yes, the protocol can be customized through hooks and middleware functionalities provided by ModelContextProtocolServer, allowing developers to tailor it to precise requirements.
If you're interested in contributing to this project:
git clone https://github.com/[your-username]/modelcontextprotocolserver.git
cd modelcontextprotocolserver
npm install
npm run dev
npm test
to run the test suite.For more information on using and contributing to ModelContextProtocolServer, refer to:
By leveraging the power of MCP Server, developers can create highly flexible and scalable AI applications that seamlessly integrate with a wide range of backend services. Whether you're building chatbots, analytics dashboards, or custom plugins, ModelContextProtocolServer provides the foundation you need to deliver innovative solutions.
This guide outlines the essential features, installation process, use cases, and integration strategies necessary for deploying ModelContextProtocolServer in your AI applications. It serves as a comprehensive starting point for development and further customization. Happy coding! 🚀✨
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