AI Agent for Honeycomb MCP
Seamlessly integrate Honeycomb observability data into your workflows with the Honeycomb MCP Model Context Protocol server. Enable AI agents and LLMs to query, analyze, and monitor your Honeycomb datasets across multiple environments, all while optimizing performance and reducing manual effort. Unlock real-time analytics, SLO monitoring, and dataset insights for data-driven operations.
Unified Observability Data Access
The Honeycomb MCP server empowers your AI agents to access and query Honeycomb datasets across multiple environments with a single interface. Instantly run analytics queries, monitor SLOs, and view triggers—maximizing visibility and operational agility for enterprise observability.
- Multi-Environment Support.
- Query datasets and monitor SLOs across production, staging, and custom environments—all from a unified endpoint.
- Powerful Analytics Queries.
- Run real-time analytics with support for calculations, breakdowns, time-based analysis, and advanced filtering.
- Optimized for Enterprise.
- Designed for Honeycomb Enterprise customers, delivering secure, high-performance data access for mission-critical workloads.
- Performance Caching.
- Leverage configurable caching to minimize API calls and accelerate query response times across all environments.
AI-Powered Data Analysis & Monitoring
Empower LLMs and AI agents to directly analyze Honeycomb datasets: automatically calculate metrics, monitor SLOs, and receive insights on triggers and data patterns. Enable proactive incident management and informed decision-making at scale.
- Automated Insights.
- Analyze columns, trigger status, and SLO health using advanced AI-driven queries—no manual data crunching required.
- Streamlined Tooling.
- Utilize built-in tools like list_datasets, get_columns, run_query, analyze_columns, and more for efficient data exploration.
- Real-Time Alerts.
- Instantly surface triggers and anomalies to stay ahead of potential incidents and ensure system reliability.
Developer-Friendly Integration & Setup
Quickly deploy and configure the Honeycomb MCP server for your enterprise. Simple installation, flexible environment configuration, and rich client compatibility with Claude, Cursor, Windsurf, and more. Enhance your observability stack with minimal setup time.
- Easy Setup.
- Install and launch with Node.js 18+, configure API keys and environments, and get started in minutes.
- Client Compatibility.
- Works seamlessly with Claude Desktop, Claude Code, Cursor, Windsurf, and Goose for versatile integration.
MCP INTEGRATION
Available Honeycomb MCP Integration Tools
The following tools are available as part of the Honeycomb MCP integration:
- list_datasets
List all datasets in a specified environment for analysis and querying.
- get_columns
Retrieve column information and schema details for a particular dataset.
- run_query
Run analytics queries with calculations, breakdowns, and filters on datasets.
- analyze_columns
Analyze columns in a dataset by running statistical queries and returning key metrics.
- list_slos
List all Service Level Objectives (SLOs) for a given dataset.
- get_slo
Get detailed information and status of a specific SLO within a dataset.
- list_triggers
List all triggers configured for a specific dataset.
- get_trigger
Retrieve detailed information about a particular trigger in a dataset.
- get_trace_link
Generate a deep link to a specific trace in the Honeycomb user interface.
- get_instrumentation_help
Provides OpenTelemetry instrumentation guidance for supported languages.
Experience Honeycomb MCP in Action
See how you can analyze and query your Honeycomb observability data seamlessly with Model Context Protocol. Book a demo or try FlowHunt free to unlock powerful, real-time insights across your environments.
What is Honeycomb
Honeycomb is an advanced observability platform designed for modern software engineering teams to understand, debug, and improve complex distributed systems. The company provides real-time insights into application performance, empowering developers and operators to pinpoint issues, analyze system behavior, and optimize user experiences. Honeycomb excels at handling high-cardinality data, enabling users to ask complex questions about their systems and receive fast, actionable answers. Its platform is built to ingest and analyze data from cloud-native architectures, microservices, and serverless environments, making it a crucial tool for teams operating at scale. Honeycomb’s mission is to give all software engineers the observability they need to improve their processes and delight their users.
Capabilities
What we can do with Honeycomb
With Honeycomb, users can monitor, analyze, and optimize distributed systems, quickly identify root causes of issues, and gain deep visibility into their application’s behavior. The platform supports a wide range of use cases, from debugging production incidents to optimizing application performance and ensuring reliability at scale.
- Monitoring distributed systems
- Continuously observe system health and catch anomalies in real-time.
- Root cause analysis
- Quickly drill down into issues and discover the underlying causes with high-cardinality querying.
- Performance optimization
- Identify bottlenecks and optimize application performance using detailed telemetry.
- Collaboration and sharing
- Enable collaborative investigations with team-based tools and shared queries.
- Integration with modern stacks
- Seamlessly integrate with OpenTelemetry, Kubernetes, AWS, and other cloud-native tools.
How AI Agents Benefit from Honeycomb
AI agents can leverage Honeycomb’s rich observability data to self-diagnose and remediate anomalies in distributed systems. By accessing high-granularity telemetry, AI-driven systems can make informed decisions, automate issue detection and resolution, and continuously learn from application behavior. Honeycomb’s robust API and integrations allow AI agents to ingest, analyze, and act on real-time performance data, improving reliability and operational efficiency.