Honeycomb MCP
A Model Context Protocol server for interacting with Honeycomb observability data. This server enables LLMs like Claude to directly analyze and query your Honeycomb datasets across multiple environments.
Honeycomb MCP Logo
Requirements
- Node.js 18+
- Honeycomb API key with full permissions:
Honeycomb MCP is effectively a complete alternative interface to Honeycomb, and thus you need broad permissions for the API.
Honeycomb Enterprise Only
Currently, this is only available for Honeycomb Enterprise customers.
How it works
Today, this is a single server process that you must run on your own computer. It is not authenticated. All information uses STDIO between your client and the server.
Installation
The build artifact goes into the
/build folder.Configuration
To use this MCP server, you need to provide Honeycomb API keys via environment variables in your MCP config.
For multiple environments:
Important: These environment variables must bet set in the
env block of your MCP config.EU Configuration
EU customers must also set a
HONEYCOMB_API_ENDPOINT configuration, since the MCP defaults to the non-EU instance.Caching Configuration
The MCP server implements caching for all non-query Honeycomb API calls to improve performance and reduce API usage. Caching can be configured using these environment variables:
Client compatibility
Honeycomb MCP has been tested with the following clients:
It will likely work with other clients.
Features
- Query Honeycomb datasets across multiple environments
- Run analytics queries with support for:
- Monitor SLOs and their status (Enterprise only)
- Analyze columns and data patterns
- View and analyze Triggers
- Access dataset metadata and schema information
- Optimized performance with TTL-based caching for all non-query API calls
Resources
Access Honeycomb datasets using URIs in the format:
honeycomb://{environment}/{dataset}For example:
honeycomb://production/api-requests
honeycomb://staging/backend-services
The resource response includes:
- Dataset name
- Column information (name, type, description)
- Schema details
Tools
list_datasets: List all datasets in an environment
get_columns: Get column information for a dataset
run_query: Run analytics queries with rich options
analyze_columns: Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.
list_slos: List all SLOs for a dataset
get_slo: Get detailed SLO information
list_triggers: List all triggers for a dataset
get_trigger: Get detailed trigger information
get_trace_link: Generate a deep link to a specific trace in the Honeycomb UI
get_instrumentation_help: Provides OpenTelemetry instrumentation guidance
Example Queries with Claude
Ask Claude things like:
- "What datasets are available in the production environment?"
- "Show me the P95 latency for the API service over the last hour"
- "What's the error rate broken down by service name?"
- "Are there any SLOs close to breaching their budget?"
- "Show me all active triggers in the staging environment"
- "What columns are available in the production API dataset?"
Optimized Tool Responses
All tool responses are optimized to reduce context window usage while maintaining essential information:
- List datasets: Returns only name, slug, and description
- Get columns: Returns streamlined column information focusing on name, type, and description
- Run query:
- Analyze column:
- SLO information: Streamlined to key status indicators and performance metrics
- Trigger information: Focused on trigger status, conditions, and notification targets
This optimization ensures that responses are concise but complete, allowing LLMs to process more data within context limitations.
Query Specification for `run_query`
The
run_query tool supports a comprehensive query specification:- calculations: Array of operations to perform
- filters: Array of filter conditions
- filter_combination: "AND" or "OR" (default is "AND")
- breakdowns: Array of columns to group results by
- orders: Array specifying how to sort results
- time_range: Relative time range in seconds (e.g., 3600 for last hour)
- start_time and end_time: UNIX timestamps for absolute time ranges
- having: Filter results based on calculation values
Example Queries
Here are some real-world example queries:
Find Slow API Calls
Distribution of DB Calls (Last Week)
Exception Count by Exception and Caller
Development
License
MIT
Honeycomb MCP
A Model Context Protocol server for interacting with Honeycomb observability data. This server enables LLMs like Claude to directly analyze and query your Honeycomb datasets across multiple environments.
Honeycomb MCP Logo
Requirements
- Node.js 18+
- Honeycomb API key with full permissions:
Honeycomb MCP is effectively a complete alternative interface to Honeycomb, and thus you need broad permissions for the API.
Honeycomb Enterprise Only
Currently, this is only available for Honeycomb Enterprise customers.
How it works
Today, this is a single server process that you must run on your own computer. It is not authenticated. All information uses STDIO between your client and the server.
Installation
The build artifact goes into the
/build folder.Configuration
To use this MCP server, you need to provide Honeycomb API keys via environment variables in your MCP config.
For multiple environments:
Important: These environment variables must bet set in the
env block of your MCP config.EU Configuration
EU customers must also set a
HONEYCOMB_API_ENDPOINT configuration, since the MCP defaults to the non-EU instance.Caching Configuration
The MCP server implements caching for all non-query Honeycomb API calls to improve performance and reduce API usage. Caching can be configured using these environment variables:
Client compatibility
Honeycomb MCP has been tested with the following clients:
It will likely work with other clients.
Features
- Query Honeycomb datasets across multiple environments
- Run analytics queries with support for:
- Monitor SLOs and their status (Enterprise only)
- Analyze columns and data patterns
- View and analyze Triggers
- Access dataset metadata and schema information
- Optimized performance with TTL-based caching for all non-query API calls
Resources
Access Honeycomb datasets using URIs in the format:
honeycomb://{environment}/{dataset}For example:
honeycomb://production/api-requests
honeycomb://staging/backend-services
The resource response includes:
- Dataset name
- Column information (name, type, description)
- Schema details
Tools
list_datasets: List all datasets in an environment
get_columns: Get column information for a dataset
run_query: Run analytics queries with rich options
analyze_columns: Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.
list_slos: List all SLOs for a dataset
get_slo: Get detailed SLO information
list_triggers: List all triggers for a dataset
get_trigger: Get detailed trigger information
get_trace_link: Generate a deep link to a specific trace in the Honeycomb UI
get_instrumentation_help: Provides OpenTelemetry instrumentation guidance
Example Queries with Claude
Ask Claude things like:
- "What datasets are available in the production environment?"
- "Show me the P95 latency for the API service over the last hour"
- "What's the error rate broken down by service name?"
- "Are there any SLOs close to breaching their budget?"
- "Show me all active triggers in the staging environment"
- "What columns are available in the production API dataset?"
Optimized Tool Responses
All tool responses are optimized to reduce context window usage while maintaining essential information:
- List datasets: Returns only name, slug, and description
- Get columns: Returns streamlined column information focusing on name, type, and description
- Run query:
- Analyze column:
- SLO information: Streamlined to key status indicators and performance metrics
- Trigger information: Focused on trigger status, conditions, and notification targets
This optimization ensures that responses are concise but complete, allowing LLMs to process more data within context limitations.
Query Specification for `run_query`
The
run_query tool supports a comprehensive query specification:- calculations: Array of operations to perform
- filters: Array of filter conditions
- filter_combination: "AND" or "OR" (default is "AND")
- breakdowns: Array of columns to group results by
- orders: Array specifying how to sort results
- time_range: Relative time range in seconds (e.g., 3600 for last hour)
- start_time and end_time: UNIX timestamps for absolute time ranges
- having: Filter results based on calculation values
Example Queries
Here are some real-world example queries:
Find Slow API Calls
Distribution of DB Calls (Last Week)
Exception Count by Exception and Caller
Development
License
MIT