ragie-mcp-server
The Ragie Model Context Protocol Server implements the MCP to enable retrieval of information from the Ragie knowledge base. It provides a querying tool, allowing users to easily extract relevant information.
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31
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16
Issues
1
Installation
Difficulty
IntermediateEstimated Time
10-20 minutes
Requirements
Node.js >= 18Installation
Installation
Prerequisites
Please specify required software and versions:Node.js: 18.0.0 or higher
Installation Steps
1. Start the Server
Run the following command to start the server:bash
RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server
2. Using Command Line Options
To specify a custom description or partition ID, run:bash
RAGIE_API_KEY=your_api_key npx @ragieai/mcp-server --description "Custom description" --partition your_partition_id
Troubleshooting
Common Issues
Issue: Server won't start Solution: Check Node.js version and reinstall dependencies.Configuration
Configuration
Basic Configuration
Create MCP Configuration File
Create a file calledmcp.json with the following content:
json
{
"mcpServers": {
"ragie": {
"command": "npx",
"args": ["-y", "@ragieai/mcp-server", "--partition", "optional_partition_id"],
"env": {
"RAGIE_API_KEY": "your_api_key"
}
}
}
}
Setting Environment Variables
Set the following environment variable as needed:bash
export RAGIE_API_KEY="your_api_key"
Security Settings
Store API keys in environment variables or secure configuration files.
Set appropriate file access permissions.
Examples
Examples
Basic Usage
Here are basic usage examples for the MCP server:Programmatic Usage
javascript
// JavaScript example (Node.js)
const { MCPClient } = require('@modelcontextprotocol/client');
const client = new MCPClient();
await client.connect();
// Execute tool
const result = await client.callTool('toolName', {
parameter1: 'value1',
parameter2: 'value2'
});
console.log(result);
Script Usage
bash
#!/bin/bash
Batch processing example
for file in *.txt; do
mcp-tool process "$file"
done
Use Cases
Execute queries to search for specific information from a company's knowledge base.
Dynamically retrieve data needed by AI models using the MCP server.
Utilize a common MCP server configuration across multiple projects.
Run automated data processing using scripts.