How to Use the Helicone (LLM Observability) MCP in LlamaIndex

Q: Can I query my LLM latency using Helicone (LLM Observability) and LlamaIndex?

Yes, your agent can call querylatency to fetch real-time performance metrics. This data can be indexed alongside your documents to monitor system health.

Q: How does Helicone (LLM Observability) help manage prompt versions in LlamaIndex?

The agent uses getpromptversions to pull prompt histories. You can then search these versions semantically to find the most effective prompt structure.

Q: How do I track user feedback on indexed search results?

Use logfeedback to record user ratings on retrieved nodes. You can then run queryfeedback to find out which indexed documents yield the most helpful answers.

Index your Helicone LLM observability metrics directly into LlamaIndex vector stores using our MCP Server.

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Connect Helicone (LLM Observability) MCP to LlamaIndex

Create your Vinkius account to connect Helicone (LLM 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 Helicone (LLM Observability) with LlamaIndex

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Index live LLM costs into LlamaIndex

Calling `query_costs` lets you feed your LLM financial data straight into your LlamaIndex vector store for semantic analysis. Your application can digest raw cost metrics alongside user data and convert them into searchable documents. This lets you ask your agent natural language questions about which users are costing the most. You get instant answers backed by actual Helicone data.

Search through historic prompt versions

Using `query_prompts` helps you stop losing track of what your models actually said by pulling historic prompt payloads into your index. Here's the thing: search history doesn't help if you don't know which prompt version caused the bug. When you need to find out why a model behaved weirdly last week, `get_prompt_versions` lets your agent pull the exact prompt state. It turns your raw observability data into a queryable knowledge base.

Analyze session latencies via MCP Server

Running `query_sessions` helps you analyze slow queries in your knowledge-augmented RAG pipelines by grouping requests by user journey. Your agent can isolate the exact index lookups that are lagging. Combine this with `query_latency` to get precise timing breakdowns. It helps you pinpoint whether the slowdown is in your vector database or the LLM provider itself.

Setup guide

Set up Helicone (LLM 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 Helicone (LLM 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 Helicone (LLM Observability) tools.",
)
response = await agent.run("List recent Helicone (LLM Observability) data")

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Common questions about Helicone (LLM Observability) MCP in LlamaIndex

You initialize the Helicone MCP Server tool spec and convert it to a tool list. From there, your LlamaIndex agent can run `query_requests` to load logs directly into your vector store.

Yes, your agent can call `query_latency` to fetch real-time performance metrics. This data can be indexed alongside your documents to monitor system health.

The agent uses `get_prompt_versions` to pull prompt histories. You can then search these versions semantically to find the most effective prompt structure.

Use `log_feedback` to record user ratings on retrieved nodes. You can then run `query_feedback` to find out which indexed documents yield the most helpful answers.

Your LLM prompt histories and session IDs are protected by Vinkius's zero-trust sandbox. The server processes these data types ephemerally, ensuring no sensitive payloads are leaked or cached.

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