DataBridge MCP Server
DataBridge MCP servers enable AI models to interact with local databases for contextual information, supporting persistent storage and unified access to ML services.
Overview
The MCP DataBridge Server integrates with DataBridge to enable ingestion and retrieval of contextual information from a local database, supporting persistent storage for AI applications. It implements the Model Context Protocol (MCP), enabling connections to different ML services through a unified interface.
Developed by:
Key Features
Contextual Information Ingestion
Ingest and manage contextual data from local databases for AI models.
Efficient Data Retrieval
Retrieve relevant contextual information quickly for AI applications.
Unified ML Service Interface
Connect to various ML services through a single, standardized interface.
Persistent Storage Support
Utilize local databases for persistent storage of AI-related data.
Available Tools
Quick Reference
| Tool | Purpose | Category |
|---|---|---|
ingest_data | Ingest contextual information | Write |
retrieve_data | Retrieve contextual information | Read |
list_ml_services | List available ML services | Discovery |
Detailed Usage
ingest_data▶
Ingest contextual data into the local database.
use_mcp_tool({
server_name: "databridge",
tool_name: "ingest_data",
arguments: {
data: {
"document_id": "doc123",
"content": "This is the content of the document."
},
collection: "documents"
}
});
retrieve_data▶
Retrieve contextual data from the local database.
use_mcp_tool({
server_name: "databridge",
tool_name: "retrieve_data",
arguments: {
query: "document_id = 'doc123'",
collection: "documents"
}
});
list_ml_services▶
List available ML services connected through the DataBridge MCP server.
use_mcp_tool({
server_name: "databridge",
tool_name: "list_ml_services",
arguments: {}
});
Installation
{
"mcpServers": {
"databridge": {
"command": "python",
"args": [
"-m",
"databridge.mcp"
]
}
}
}
Prerequisites:
Ensure Python 3.8 or higher and uv or pip are installed.
Install with uv: uv venv && source .venv/bin/activate && uv pip install mcp-server
Install with pip: pip install mcp-server
Sources
Related Articles
Sequential Thinking MCP Server: AI Step-by-Step Problem Solving
Sequential Thinking MCP servers enable AI models to perform structured, step-by-step problem-solving with support for thought revision, branching reasoning, and dynamic context management.
Model Context Protocol (MCP): Open Standard for AI Integration
The Model Context Protocol (MCP) is an open standard enabling AI systems to connect with diverse data sources, tools, and services, eliminating custom integrations for seamless interaction.
ConsoleSpy MCP Server
ConsoleSpy MCP servers enable AI models to interact with browser console logs, providing capabilities for real-time debugging, error monitoring, and application analysis.