LangSmith (LLM Observability & Hub) MCP Server for Pydantic AI 6 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect LangSmith (LLM Observability & Hub) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to LangSmith (LLM Observability & Hub) "
"(6 tools)."
),
)
result = await agent.run(
"What tools are available in LangSmith (LLM Observability & Hub)?"
)
print(result.data)
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.
Pydantic AI validates every LangSmith (LLM Observability & Hub) tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the LangSmith (LLM Observability & Hub) MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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) with type-safe schemas
Why Use Pydantic AI with the LangSmith (LLM Observability & Hub) MCP Server
Pydantic AI provides unique advantages when paired with LangSmith (LLM Observability & Hub) through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your LangSmith (LLM Observability & Hub) integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your LangSmith (LLM Observability & Hub) connection logic from agent behavior for testable, maintainable code
LangSmith (LLM Observability & Hub) + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the LangSmith (LLM Observability & Hub) MCP Server delivers measurable value.
Type-safe data pipelines: query LangSmith (LLM Observability & Hub) with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple LangSmith (LLM Observability & Hub) tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query LangSmith (LLM Observability & Hub) and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock LangSmith (LLM Observability & Hub) responses and write comprehensive agent tests
LangSmith (LLM Observability & Hub) MCP Tools for Pydantic AI (6)
These 6 tools become available when you connect LangSmith (LLM Observability & Hub) to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting LangSmith (LLM Observability & Hub) to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiLangSmith (LLM Observability & Hub) + Pydantic AI FAQ
Common questions about integrating LangSmith (LLM Observability & Hub) MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.