The Honeycomb MCP server enables Claude AI to interact with Honeycomb's API for observability and monitoring workflows. This interface provides capabilities for:
Authentication: Validate API keys and authenticate with Honeycomb
Dataset Management: List, create, update, and retrieve datasets
Column Management: List columns within datasets
Query Management: Create, retrieve, execute, and get results of queries
Event Management: Create single or batch events in datasets
Board Management: List, create, update, retrieve, and delete boards
Marker Management: List, create, update, retrieve, and delete markers
SLO Management: List, create, update, and retrieve Service Level Objectives
Trigger Management: List, create, update, retrieve, and delete triggers
Provides tools for interacting with the Honeycomb API, enabling operations such as retrieving, creating, and updating Honeycomb datasets, queries, events, boards, markers, SLOs, and triggers to streamline observability and monitoring workflows.
Honeycomb MCP Server
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Overview
This server is an interface that uses the Model Context Protocol (MCP) to enable Claude AI to interact with the Honeycomb API.
With this MCP server, Claude AI can perform operations such as retrieving, creating, and updating Honeycomb datasets, queries, events, boards, markers, SLOs, and triggers.
About the Repository
This repository provides a standalone implementation of the Honeycomb MCP server. It integrates Claude AI with Honeycomb to streamline observability and monitoring workflows.
Setup
Prerequisites
Node.js 18 or higher
Honeycomb API key
Installation
Setting Environment Variables
MCP Configuration Example
If you're using this MCP server, add the following configuration to your mcp_config.json
file:
Starting the Server
Available Tools
This MCP server provides the following tools:
Authentication
honeycomb_auth
Authenticates with the Honeycomb API
Input:
apiKey
(string, optional): Honeycomb API key (if not provided, uses environment variable)
Dataset Management
honeycomb_datasets_list
Lists all available datasets
No input parameters required
honeycomb_dataset_get
Gets information about a specific dataset
Input:
datasetSlug
(string, required): Slug of the dataset
honeycomb_datasets_create
Creates a new dataset
Input:
name
(string, required): Name of the datasetdescription
(string, optional): Description of the dataset
Column Management
honeycomb_columns_list
Lists all columns in a dataset
Input:
datasetSlug
(string, required): Slug of the dataset
Query Management
honeycomb_query_create
Creates a new query for a dataset
Input:
datasetSlug
(string, required): Slug of the datasetquery
(object, required): Query configuration
honeycomb_query_result_create
Executes a query and returns the results
Input:
datasetSlug
(string, required): Slug of the datasetquery
(object, required): Query configuration
Event Management
honeycomb_event_create
Creates a new event in a dataset
Input:
datasetSlug
(string, required): Slug of the datasetdata
(object, required): Event data
Board Management
honeycomb_boards_list
Lists all boards
No input parameters required
honeycomb_board_get
Gets information about a specific board
Input:
boardId
(string, required): ID of the board
honeycomb_board_create
Creates a new board
Input:
name
(string, required): Name of the boarddescription
(string, optional): Description of the boardquery_ids
(array of strings, optional): Query IDs to include in the board
honeycomb_board_update
Updates an existing board
Input:
boardId
(string, required): ID of the board to updatename
(string, optional): New name for the boarddescription
(string, optional): New description for the boardquery_ids
(array of strings, optional): New query IDs to include in the board
Marker Management
honeycomb_markers_list
Lists all markers for a dataset
Input:
datasetSlug
(string, required): Slug of the dataset
honeycomb_marker_create
Creates a new marker
Input:
datasetSlug
(string, required): Slug of the datasetmessage
(string, required): Message for the markertype
(string, required): Type of the markerstart_time
(string, required): Start time for the markerend_time
(string, optional): End time for the markerurl
(string, optional): URL associated with the marker
Input:
datasetSlug
(string, required): Slug of the datasetmarkerId
(string, required): ID of the marker to delete
Example Usage
Here's an example of Claude using this MCP server to create a new Honeycomb dataset and add an event:
Common Integration Patterns
Observability Automation
[User] Claude, please help me set up a new SLO for API response times. [Claude] I can help with that. Let me create an SLO to track API response times. I'll use honeycomb_slo_create with these parameters: { "datasetSlug": "production-api", "name": "API Response Time SLO", "description": "Tracks response time for critical API endpoints", "sli": { "alias": "Response Time < 300ms", "conditions": { "column": "duration_ms", "operator": "<", "value": 300 } }, "time_period_days": 30, "target_per_million": 995000 }Incident Investigation
[User] We're seeing elevated error rates in production. Can you help investigate? [Claude] I'll help investigate the elevated error rates. Let me query the recent error data. First, I'll use honeycomb_query_result_create: { "datasetSlug": "production", "query": { "calculations": [ { "op": "COUNT" } ], "filters": [ { "column": "status_code", "operator": ">=", "value": 500 } ], "breakdowns": ["service", "endpoint"], "time_range": 3600, "granularity": 60 } }
Creating dataset "Application Monitoring" with the description "Monitoring for our production application"...
[Claude uses honeycomb_datasets_create]
The dataset has been successfully created! Now we can add events to it.
Would you like me to create a sample event in the dataset to test it?
[User] Yes, please add a sample event.
[Claude] I'll add a sample event to the "Application Monitoring" dataset.
[Claude uses honeycomb_event_create]
Success! I've added a sample event to your "Application Monitoring" dataset with the following data: { "service": "api-server", "message": "User login successful", "duration_ms": 45, "status_code": 200, "endpoint": "/api/login", "timestamp": "2025-03-25T14:30:00Z" }
This event represents a successful user login operation that took 45ms to complete.
You can now view this event in your Honeycomb dashboard and start building queries to analyze your data.
Common Error Codes
AUTH_ERROR
: Authentication failed. Check your API key.NOT_FOUND
: The requested resource was not found.INVALID_PARAMETER
: One or more parameters are invalid.RATE_LIMIT
: Honeycomb API rate limit has been reached.SERVER_ERROR
: Internal server error occurred.
Troubleshooting Tips
Authentication Issues
Ensure your
HONEYCOMB_API_KEY
is set correctlyVerify the API key has appropriate permissions
Dataset Not Found
Confirm that the dataset slug is correct (check for typos)
Make sure the dataset exists in your Honeycomb account
Query Execution Issues
Validate that query parameters are formatted correctly
Check column names in queries match those in your dataset
Contributing
Contributions to the Honeycomb MCP server are welcome! Here's how you can contribute:
Development Setup
Fork the repository
Clone your fork
git clone https://github.com/your-username/honeycomb-mcp-server.gitInstall dependencies
npm installMake your changes
Run the build
npm run buildTest your changes locally
Pull Request Process
Create a feature branch
git checkout -b feat-your-feature-nameCommit your changes following Conventional Commits format
git commit -m "feat: add new feature"Push to your fork
git push origin feat-your-feature-nameOpen a Pull Request
Coding Standards
Use TypeScript for all new code
Follow the existing code style
Add comments for public APIs
Write tests for new functionality
License
This project is licensed under the MIT License - see the LICENSE file for details.
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Tools
honeycomb-mcp-server
Related Resources
Related MCP Servers
- MIT License
- MIT License
- PythonMIT License