KevoDB Server

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KevoDB MCP Server

This project implements a MCP (Multimodal Communication Protocol) server for KevoDB, allowing AI agents to interact with KevoDB using a standardized API.

Features

• Exposes KevoDB operations through MCP tools
• Supports all core KevoDB functionality:
  • Basic key-value operations (get, put, delete)
  • Range, prefix, and suffix scans
  • Transactions
  • Batch operations
  • Database statistics
• Simple string-based API with UTF-8 encoding

Prerequisites

• Python 3.8+
• Running KevoDB server (default: localhost:50051)
• FastMCP library
• Python-Kevo SDK

Installation

1. Install dependencies:

pip install fastmcp python-kevo

2. Ensure KevoDB is running on localhost:50051 (or set the KEVO_HOST and KEVO_PORT environment variables to connect to a different endpoint)

Usage

Running the MCP Server

Start the MCP server:

python main.py

This will launch the MCP server on http://localhost:9000/mcp

You can configure the KevoDB connection using environment variables:

• KEVO_HOST: Hostname of the KevoDB server (default: "localhost")
• KEVO_PORT: Port of the KevoDB server (default: "50051")

Example:

KEVO_HOST=192.168.1.100 KEVO_PORT=5000 python main.py

Using with AI Agents

AI agents that support MCP can connect to this server and use all exposed tools. The server provides the following tools:

Tool	Description
connect	Connect to the KevoDB server
get	Get a value by key from KevoDB
put	Store a key-value pair in KevoDB
delete	Delete a key-value pair from KevoDB
scan	Scan keys in KevoDB with options
batch_write	Perform multiple operations in a batch
get_stats	Get database statistics
begin_transaction	Begin a new transaction and return transaction ID
commit_transaction	Commit a transaction by ID
rollback_transaction	Roll back a transaction by ID
tx_put	Store a key-value pair within a transaction
tx_get	Get a value by key within a transaction
tx_delete	Delete a key-value pair within a transaction
cleanup	Close the KevoDB connection

Integration with AI Applications

To use KevoDB with your AI application:

1. Start the KevoDB server
2. Start this MCP server
3. Configure your AI agent to connect to the MCP endpoint
4. The AI agent can now use all KevoDB operations through the MCP interface

License

MIT