honeycomb-mcp
Honeycomb MCP is a Model Context Protocol server designed for interacting with Honeycomb observability data. It allows LLMs like Claude to directly analyze and query your Honeycomb datasets across multiple environments. It runs in a Node.js environment and requires a Honeycomb API key with full permissions.
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Not Rated
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Issues
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Installation
Difficulty
IntermediateEstimated Time
10-20 minutes
Requirements
Node.js 18+Installation
Installation
Prerequisites
Please specify required software and versions:Node.js: 18.0.0 or higher
Installation Steps
1. Clone Repository
bash
git clone https://github.com/honeycombio/honeycomb-mcp.git
cd honeycomb-mcp
2. Install Dependencies
bash
pnpm install
3. Build the Project
bash
pnpm run build
/build folder.
Troubleshooting
Common Issues
Issue: Server won't start Solution: Check Node.js version and reinstall dependencies. Issue: Invalid API key Solution: Verify the permissions of the API key.Configuration
Configuration
Basic Configuration
To use this MCP server, you need to provide Honeycomb API keys via environment variables in your MCP config.json
{
"mcpServers": {
"honeycomb": {
"command": "node",
"args": [
"/fully/qualified/path/to/honeycomb-mcp/build/index.mjs"
],
"env": {
"HONEYCOMB_API_KEY": "your_api_key"
}
}
}
}
EU Configuration
EU customers must also set aHONEYCOMB_API_ENDPOINT configuration.
bash
HONEYCOMB_API_ENDPOINT=https://api.eu1.honeycomb.io/
Caching Configuration
To enable caching, set the following environment variables:bash
HONEYCOMB_CACHE_ENABLED=true
HONEYCOMB_CACHE_DEFAULT_TTL=300
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);
Use Cases
Setting up alerts to monitor performance in production environments and detect anomalies.
Identifying system bottlenecks through data analysis.
Implementing real-time data visualization using dashboards.
Comparative analysis of datasets across different environments (development, staging, production).