Honeycomb MCP: Unleash AI-Powered Observability with UBOS

The Honeycomb Model Context Protocol (MCP) server revolutionizes how you interact with your Honeycomb observability data by enabling Large Language Models (LLMs) like Claude to directly query, analyze, and visualize your datasets. This powerful combination unlocks unprecedented insights and empowers your team to make data-driven decisions faster than ever before. By integrating the Honeycomb MCP with the UBOS AI Agent Development Platform, you create an AI-driven observability powerhouse, bringing AI agents directly into your troubleshooting and performance optimization workflows.

What is Honeycomb MCP?

The Model Context Protocol (MCP) is an open standard designed to standardize the way applications provide context to LLMs. In the case of Honeycomb, the MCP server acts as a bridge, enabling LLMs to access and manipulate your Honeycomb datasets. This means you can use natural language to ask questions, analyze performance bottlenecks, identify anomalies, and even automate troubleshooting tasks, all through the power of AI.

Why is Honeycomb MCP Important?

In today’s complex and dynamic environments, understanding application behavior is critical. Honeycomb provides rich observability data, but extracting actionable insights can be time-consuming. Honeycomb MCP streamlines this process by empowering LLMs to directly access, query, and interpret Honeycomb data, significantly accelerating your analysis and improving your overall observability posture. This is particularly important for Enterprise customers who handle large volumes of data across multiple environments.

Key Features and Benefits of Honeycomb MCP

• AI-Powered Data Analysis: Use natural language queries to analyze Honeycomb datasets, identify patterns, and uncover hidden insights with LLMs like Claude.
• Cross-Environment Querying: Seamlessly query Honeycomb datasets across multiple environments (production, staging, development) from a single interface.
• Comprehensive Toolset: Leverage a rich set of tools for data exploration, analysis, and troubleshooting, including:
  • list_datasets: Discover available datasets within specific environments.
  • get_columns: Explore the schema and structure of individual datasets.
  • run_query: Execute complex analytics queries with diverse calculations, breakdowns, and filters.
  • analyze_columns: Perform in-depth statistical analysis on specific columns.
  • list_slos: Monitor Service Level Objectives (SLOs) and their status.
  • get_slo: Obtain detailed SLO information and performance metrics.
  • list_triggers: Manage and monitor active triggers.
  • get_trigger: Access detailed trigger configurations and status.
  • get_trace_link: Quickly navigate to specific traces in the Honeycomb UI.
  • get_instrumentation_help: Receive guidance on OpenTelemetry instrumentation.
• Optimized Tool Responses: Responses from the MCP server are carefully optimized to minimize context window usage in LLMs, ensuring efficient processing of information.
• Caching for Performance: The MCP server implements caching mechanisms for non-query API calls, improving performance and reducing API usage.
• Integration with UBOS Platform: Connect Honeycomb MCP to the UBOS platform and transform your observability data into actionable workflows orchestrated by AI Agents.

Use Cases for Honeycomb MCP

• Incident Response:
  • Quickly identify the root cause of incidents by querying Honeycomb data with natural language.
  • Automate initial triage steps using AI Agents connected to Honeycomb MCP.
  • Example: “Show me the error rate broken down by service name in the last 5 minutes.”
• Performance Optimization:
  • Identify performance bottlenecks by analyzing latency distributions and resource usage.
  • Proactively address performance issues before they impact users.
  • Example: “What is the P95 latency for the API service over the last hour?”
• SLO Monitoring:
  • Monitor SLOs and receive alerts when they are close to breaching their budget.
  • Automate remediation steps based on SLO breaches.
  • Example: “Are there any SLOs close to breaching their budget?”
• Security Analysis:
  • Identify suspicious activity by analyzing request patterns and user behavior.
  • Automate threat detection and response using AI Agents.
  • Example: “Show me requests from unusual IP addresses.”
• Data Exploration:
  • Explore Honeycomb datasets with natural language queries to uncover hidden insights.
  • Quickly understand the relationships between different data points.
  • Example: “What columns are available in the production API dataset?”

Example Queries

Here are some examples of queries you can run with Honeycomb MCP:

• “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?”

• Find Slow API Calls:

  { “environment”: “production”, “dataset”: “api-requests”, “calculations”: [ {“column”: “duration_ms”, “op”: “HEATMAP”}, {“column”: “duration_ms”, “op”: “MAX”} ], “filters”: [ {“column”: “trace.parent_id”, “op”: “does-not-exist”} ], “breakdowns”: [“http.target”, “name”], “orders”: [ {“column”: “duration_ms”, “op”: “MAX”, “order”: “descending”} ] }

• Distribution of DB Calls (Last Week):

  { “environment”: “production”, “dataset”: “api-requests”, “calculations”: [ {“column”: “duration_ms”, “op”: “HEATMAP”} ], “filters”: [ {“column”: “db.statement”, “op”: “exists”} ], “breakdowns”: [“db.statement”], “time_range”: 604800 }

• Exception Count by Exception and Caller:

  { “environment”: “production”, “dataset”: “api-requests”, “calculations”: [ {“op”: “COUNT”} ], “filters”: [ {“column”: “exception.message”, “op”: “exists”}, {“column”: “parent_name”, “op”: “exists”} ], “breakdowns”: [“exception.message”, “parent_name”], “orders”: [ {“op”: “COUNT”, “order”: “descending”} ] }

Integrating Honeycomb MCP with the UBOS Platform

UBOS is a full-stack AI Agent Development Platform designed to empower businesses with AI Agents across all departments. By integrating Honeycomb MCP with UBOS, you can:

• Orchestrate AI Agents: Create AI Agents that automatically analyze Honeycomb data and trigger actions based on insights.
• Connect to Enterprise Data: Connect Honeycomb MCP to other enterprise data sources to provide a holistic view of your business operations.
• Build Custom AI Agents: Customize AI Agents with your own LLM models and integrate them with Honeycomb MCP for specialized analysis.
• Create Multi-Agent Systems: Build complex AI Agent systems that collaborate to solve complex observability challenges.

How to Get Started with Honeycomb MCP and UBOS

1. Set up Honeycomb MCP: Follow the installation and configuration instructions in the Honeycomb MCP documentation.
2. Obtain Honeycomb API Key: You will need a Honeycomb API key with full permissions (Query access for analytics, Read access for SLOs and Triggers, and Environment-level access for dataset operations).
3. Configure UBOS: Connect the Honeycomb MCP server to your UBOS platform.
4. Create AI Agents: Start building AI Agents that leverage Honeycomb MCP to analyze your observability data and automate tasks.

Conclusion

The Honeycomb MCP, when combined with the UBOS AI Agent Development Platform, represents a significant leap forward in observability. By leveraging the power of AI, you can unlock deeper insights, automate troubleshooting, and optimize performance like never before. Embrace the future of observability with Honeycomb MCP and UBOS, and empower your team to make data-driven decisions with confidence.