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.
暂无评论. 成为第一个评论的人！
登录以参与讨论
Highlight AIList all datasets in a specified environment. Parameters: environment (string)
Get column information for a specified dataset in a specified environment. Parameters: environment (string), dataset (string)
Run analytics queries with rich options for a specified dataset in a specified environment. Parameters: environment (string), dataset (string), calculations (array), breakdowns (array), time_range (integer)
Analyzes specific columns in a dataset by running statistical queries and returning computed metrics.
List all SLOs for a specified dataset in a specified environment. Parameters: environment (string), dataset (string)
Get detailed SLO information for a specified dataset in a specified environment. Parameters: environment (string), dataset (string), sloId (string)
List all triggers for a specified dataset in a specified environment. Parameters: environment (string), dataset (string)
Get detailed trigger information for a specified dataset in a specified environment. Parameters: environment (string), dataset (string), triggerId (string)
Generate a deep link to a specific trace in the Honeycomb UI.
Provides OpenTelemetry instrumentation guidance for a specified language and file path. Parameters: language (string), filepath (string)
