How to Use the AgentOps (Agent Telemetry and Monitoring) MCP in LlamaIndex

Index your LlamaIndex agent performance logs and trace data directly into your knowledge base.

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Connect AgentOps (Agent Telemetry and Monitoring) MCP to LlamaIndex

Create your Vinkius account to connect AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) with LlamaIndex

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Ground LlamaIndex queries in real metrics

Use `get_trace_metrics` to pull live data into your LlamaIndex vector store. Your index now contains the actual execution history of your agents. This makes your RAG application smarter. You can query past performance to guide future agent behavior.

Index complex LlamaIndex trace data

Call `get_trace` to retrieve full execution logs for your LlamaIndex agents. These logs become searchable documents within your existing indices. Stop searching through external dashboards. Your history lives right next to your source data.

Query LlamaIndex span history

Run `get_span` to extract specific execution events for your LlamaIndex workflows. It turns raw logs into structured knowledge you can reference anytime. You gain a permanent memory of how your agent behaves. It’s perfect for auditing long-term agent effectiveness.

Setup guide

Set up AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) 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 AgentOps (Agent Telemetry and Monitoring) tools.",
)
response = await agent.run("List recent AgentOps (Agent Telemetry and Monitoring) data")

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Common questions about AgentOps (Agent Telemetry and Monitoring) MCP in LlamaIndex

You map the tool outputs directly into your indexing pipeline. This creates a searchable archive of your agent's operational history.

Yes, the tools allow your agent to fetch its own history. You can then index that data for later retrieval.

The server only touches the telemetry data you explicitly send to it. Your private knowledge base stays local to your index.

It includes full support for asynchronous operations. Your agent won't hang while it fetches telemetry data.

The server handles only trace identifiers and performance metrics. Your actual document content is never stored by the monitor.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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