How to Use the Dynatrace (APM and Observability) MCP in LlamaIndex

Q: Can I index historical problems from Dynatrace (APM and Observability) into LlamaIndex?

Yes. You can run a pipeline that calls listproblems and loads the resulting incident histories directly into a vector store for semantic search.

Q: Does this integration support custom dashboards in Dynatrace (APM and Observability) via LlamaIndex?

Yes, the agent can use createdashboard and updatedashboard to build or modify visual layouts based on the structured data it retrieves.

Index your live infrastructure metrics and problem logs into LlamaIndex for context-rich, hallucination-free debugging.

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Connect Dynatrace (APM and Observability) MCP to LlamaIndex

Create your Vinkius account to connect Dynatrace (APM and Observability) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Dynatrace (APM and Observability) with LlamaIndex

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Indexing telemetry into LlamaIndex vector stores

The `list_entities` tool retrieves your entire monitored infrastructure topology to build a local knowledge graph. Your agent indexes these entity relationships, matching live server states with historical performance data. When you query your system health, the agent pulls fresh data using `query_metrics` to ground its answers. This eliminates hallucinated status reports by forcing the LLM to rely on actual, real-time telemetry.

Contextual problem solving using MCP Server tools

The `list_problems` tool fetches active and historical system incidents to enrich your local RAG index. Your agent compares new alerts against past resolutions to suggest proven fixes. By combining this with `get_problem`, the agent reads deep root-cause analyses. It then matches these patterns with your internal markdown runbooks to accelerate incident response.

Semantic analysis of synthetic monitor runs

The `list_synthetic_monitors` tool catalogues your active end-to-end user path tests. Your agent indexes these configurations alongside execution results fetched via `list_synthetic_executions`. This lets you ask natural language questions about user experience trends. The agent searches the indexed execution logs to pinpoint exactly which locations or steps are degrading.

Setup guide

Set up Dynatrace (APM and Observability) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Dynatrace (APM and Observability) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Dynatrace (APM and Observability) tools.",
)
response = await agent.run("List recent Dynatrace (APM and Observability) data")

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Common questions about Dynatrace (APM and Observability) MCP in LlamaIndex

The framework uses the MCP tool adapter to turn server tools like `query_metrics` into executable functions. The agent runs these functions on demand to pull live data during queries.

Yes. You can run a pipeline that calls `list_problems` and loads the resulting incident histories directly into a vector store for semantic search.

Yes, the agent can use `create_dashboard` and `update_dashboard` to build or modify visual layouts based on the structured data it retrieves.

You initialize the client using the HTTP transport URL provided by Vinkius. The adapter handles all tool schema conversions automatically.

This MCP server handles infrastructure entity metadata, synthetic execution logs, and metric data points. Your credentials are fully managed by Vinkius, and all tool execution occurs within isolated, ephemeral sandboxes that destroy data upon session termination.

Use it with your favorite AI tools

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