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.
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List all datasets in an environment. Parameters: environment (string)
Get column information for a dataset. Parameters: environment (string), dataset (string)
Run analytics queries with rich options. Parameters: environment (string), dataset (string), calculations (array), breakdowns (array), time_range (integer), filters (optional array), orders (optional array), having (optional object), start_time (optional integer), end_time (optional integer)
Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.
List all SLOs for a dataset. Parameters: environment (string), dataset (string)
Get detailed SLO information. Parameters: environment (string), dataset (string), sloId (string)
List all triggers for a dataset. Parameters: environment (string), dataset (string)
Get detailed trigger information. Parameters: environment (string), dataset (string), triggerId (string)
Generate a deep link to a specific trace in the Honeycomb UI.
Provides OpenTelemetry instrumentation guidance. Parameters: language (string), filepath (string)
