How to Use the LangSmith MCP in LlamaIndex

Index your LlamaIndex RAG traces with LangSmith to ground your knowledge base in verifiable performance data.

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Connect LangSmith MCP to LlamaIndex

Create your Vinkius account to connect LangSmith 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 LangSmith with LlamaIndex

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Audit LlamaIndex RAG retrieval quality

Use `langsmith_list_runs` to examine how your index responds to specific user queries. You see exactly which documents the retriever pulled. This helps you verify that your index is actually returning relevant data. If the retrieved context is wrong, you know immediately what to re-index.

Analyze LlamaIndex project latency stats

The `langsmith_list_projects` tool displays median latency and success rates for your knowledge-augmented pipelines. You get a clear view of your system's health. Performance tuning becomes a routine task rather than a guessing game. You compare different index configurations side-by-side using real-world metrics.

Deep dive into LlamaIndex trace metadata

Invoke `langsmith_get_run` to pull the full trace details for any single query session. It exposes the underlying logic of your RAG application. You gain full visibility into why your system generated a specific answer. This is the only way to troubleshoot hallucinations in your knowledge base.

Setup guide

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

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Common questions about LangSmith MCP in LlamaIndex

You list your projects to see overall success rates, then drill down into recent runs for a specific LlamaIndex pipeline. This tells you which queries are performing well and which ones need better document retrieval.

Yes. Every run trace contains detailed timing data for the retrieval and generation phases. You can see if your vector store is causing a bottleneck.

Absolutely. By pulling the specific run trace, you see the exact input query and the resulting tool outputs. This makes it trivial to spot where the retrieval process went off the rails.

It is critical if you want to avoid shipping blind. You need the observability provided by this MCP Server to understand how your index handles edge cases.

This server only processes execution logs and performance metrics. It does not store your actual documents or vector index content, keeping your private knowledge base isolated.

Use it with your favorite AI tools

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

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