Honeycomb
Summary
This Honeycomb MCP server, developed by Austin Parker, enables AI assistants to directly analyze and query Honeycomb observability data. Built with TypeScript and leveraging the MCP SDK, it provides tools for listing columns, running analytics queries, and analyzing data patterns within Honeycomb datasets. The server abstracts the complexities of interacting with the Honeycomb API, allowing AI systems to easily access and interpret observability data. By bridging AI capabilities with Honeycomb's powerful analytics, this implementation facilitates use cases such as automated performance analysis, anomaly detection, and data-driven troubleshooting in complex distributed systems.
Available Actions(10)
list_datasets
List all datasets in an environment. Parameters: environment (string)
get_columns
Get column information for a dataset. Parameters: environment (string), dataset (string)
run_query
Run analytics queries with rich options. Parameters: environment (string), dataset (string), calculations (array), breakdowns (array), time_range (integer)
analyze_columns
Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.
list_slos
List all SLOs for a dataset. Parameters: environment (string), dataset (string)
get_slo
Get detailed SLO information. Parameters: environment (string), dataset (string), sloId (string)
list_triggers
List all triggers for a dataset. Parameters: environment (string), dataset (string)
get_trigger
Get detailed trigger information. Parameters: environment (string), dataset (string), triggerId (string)
get_trace_link
Generate a deep link to a specific trace in the Honeycomb UI.
get_instrumentation_help
Provides OpenTelemetry instrumentation guidance. Parameters: language (string), filepath (string)
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