LangSmith (LLM Observability & Hub) MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LangSmith (LLM Observability & Hub) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to LangSmith (LLM Observability & Hub). "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in LangSmith (LLM Observability & Hub)?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LangSmith (LLM Observability & Hub) MCP Server
Connect your LangSmith account to any AI agent and take full control of your LLM observability, tracing, and prompt management through natural conversation.
LlamaIndex agents combine LangSmith (LLM Observability & Hub) tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Trace Orchestration — List active tracing projects and retrieve detailed execution logs for specific LLM invocation runs directly from your agent
- Performance Telemetry — Extract precise metrics including token consumption, prompt latency, and exact error strings from your AI pipelines
- Prompt Hub Access — Navigate and retrieve managed prompt templates, variable definitions, and version histories hosted in the LangChain Hub
- Evaluation Datasets — Enumerate curated 'golden' datasets used for automated evaluation of prompt logic or few-shot injection models
- Human-in-the-Loop Audit — Monitor active annotation queues where human reviewers assess the alignment, accuracy, and safety of generated LLM traces
- Agentic Step Analysis — Deep-dive into multi-turn agentic workflows to understand nested tool calls and internal reasoning paths securely
The LangSmith (LLM Observability & Hub) MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect LangSmith (LLM Observability & Hub) to LlamaIndex via MCP
Follow these steps to integrate the LangSmith (LLM Observability & Hub) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from LangSmith (LLM Observability & Hub)
Why Use LlamaIndex with the LangSmith (LLM Observability & Hub) MCP Server
LlamaIndex provides unique advantages when paired with LangSmith (LLM Observability & Hub) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LangSmith (LLM Observability & Hub) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LangSmith (LLM Observability & Hub) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LangSmith (LLM Observability & Hub), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LangSmith (LLM Observability & Hub) tools were called, what data was returned, and how it influenced the final answer
LangSmith (LLM Observability & Hub) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LangSmith (LLM Observability & Hub) MCP Server delivers measurable value.
Hybrid search: combine LangSmith (LLM Observability & Hub) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LangSmith (LLM Observability & Hub) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying LangSmith (LLM Observability & Hub) for fresh data
Analytical workflows: chain LangSmith (LLM Observability & Hub) queries with LlamaIndex's data connectors to build multi-source analytical reports
LangSmith (LLM Observability & Hub) MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect LangSmith (LLM Observability & Hub) to LlamaIndex via MCP:
get_run
Get precise telemetry for a single LLM invocation run
list_annotation_queues
List active human-in-the-loop annotation queues
list_datasets
List all evaluation and fine-tuning datasets mapped in LangSmith
list_projects
Maps out the boundaries of distinct AI pipelines currently monitored by LangSmith. List all active LangSmith tracing projects/sessions
list_prompts
Extract prompt templates hosted in the LangChain Hub
list_runs
Isolates the raw interactions containing prompts sent to and responses received from the AI models. List explicit LLM invocation runs within a specific project
Example Prompts for LangSmith (LLM Observability & Hub) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LangSmith (LLM Observability & Hub) immediately.
"List all active tracing projects in LangSmith"
"Show me the telemetry for the last run in the 'Production-Bot-V2' project"
"List all prompts hosted in our Hub repository"
Troubleshooting LangSmith (LLM Observability & Hub) MCP Server with LlamaIndex
Common issues when connecting LangSmith (LLM Observability & Hub) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLangSmith (LLM Observability & Hub) + LlamaIndex FAQ
Common questions about integrating LangSmith (LLM Observability & Hub) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect LangSmith (LLM Observability & Hub) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LangSmith (LLM Observability & Hub) to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.