Smithery MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Smithery through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"smithery": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Smithery, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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 Smithery MCP Server
What you can do
Connect AI agents to the Smithery Registry for comprehensive MCP server discovery and management:
LangChain's ecosystem of 500+ components combines seamlessly with Smithery through native MCP adapters. Connect 11 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
- Search MCP servers — find servers by name, description, or tags with semantic search
- Get server details — review metadata, verification status, and user counts
- Discover tools — list all tools (functions) exposed by any registered MCP server
- Discover resources — list all data resources available from MCP servers
- Discover prompts — list all prompt templates exposed by MCP servers
- Create connections — connect to MCP servers via Smithery Connect with automatic OAuth handling
- Manage connections — list, inspect, and remove MCP server connections
- Generate service tokens — create scoped, time-limited tokens for frontend/agent access
- View analytics — monitor server usage, adoption trends, and performance metrics
The Smithery MCP Server exposes 11 tools through the Vinkius. Connect it to LangChain 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 Smithery to LangChain via MCP
Follow these steps to integrate the Smithery MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from Smithery via MCP
Why Use LangChain with the Smithery MCP Server
LangChain provides unique advantages when paired with Smithery through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Smithery MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Smithery queries for multi-turn workflows
Smithery + LangChain Use Cases
Practical scenarios where LangChain combined with the Smithery MCP Server delivers measurable value.
RAG with live data: combine Smithery tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Smithery, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Smithery tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Smithery tool call, measure latency, and optimize your agent's performance
Smithery MCP Tools for LangChain (11)
These 11 tools become available when you connect Smithery to LangChain via MCP:
create_connection
Smithery handles OAuth, tokens, and sessions automatically. Requires the server namespace and connection configuration (mcpUrl, optional headers, metadata). Returns the connection ID, status, and server info. Use this to integrate MCP servers into your applications without managing authentication complexity. Create a new connection to an MCP server via Smithery Connect
create_service_token
The token has limited permissions defined by the policy (namespaces, resources, operations, metadata, TTL). Returns the token string. Use this to provide secure, time-limited access to MCP servers without exposing your main API key. Generate a scoped service token for frontend/agent access to MCP servers
delete_connection
This action cannot be undone. Requires namespace and connection ID. Use this to clean up unused connections or revoke access. Remove an MCP server connection
get_connection
Requires namespace and connection ID. Use this to review connection details or troubleshoot connectivity issues. Get detailed information about a specific MCP connection
get_server_analytics
Requires the server qualified name. Use this to monitor server adoption, identify usage trends, or troubleshoot performance issues. Get usage analytics for a specific MCP server
get_server_details
Requires the qualified name (e.g., "smithery/hello-world" or "github/github") from search_servers results. Use this to review server capabilities before connecting. Get detailed information about a specific MCP server from the Smithery registry
get_server_prompts
Returns prompt names, descriptions, and argument definitions. Requires the server qualified name. Use this to discover reusable prompt workflows available from the server. List all prompt templates exposed by a specific MCP server
get_server_resources
Returns resource URIs, names, descriptions, and MIME types. Requires the server qualified name. Use this to understand what data the server provides read access to. List all resources exposed by a specific MCP server
get_server_tools
Returns tool names, descriptions, input schemas, and annotations. Requires the server qualified name. Use this to understand what actions the server can perform before connecting it to your agents. List all tools exposed by a specific MCP server
list_connections
Returns connection IDs, names, statuses, creation dates, and metadata. Use this to audit which connections are active, review connection configurations, or identify unused connections. List all connections for a specific MCP server namespace
search_servers
Returns matching servers with qualified names, descriptions, verification status, user counts, and deployment info. Use optional filters to narrow by namespace, verified status, or deployment state. Results include pagination metadata. Use this as the first step to discover available MCP servers before connecting or installing them. Search the Smithery registry for MCP servers by name, description, or tags
Example Prompts for Smithery in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Smithery immediately.
"Search for verified GitHub-related MCP servers"
"Show me all tools exposed by the Stripe MCP server"
"Create a connection to the Slack MCP server for my workspace"
Troubleshooting Smithery MCP Server with LangChain
Common issues when connecting Smithery to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersSmithery + LangChain FAQ
Common questions about integrating Smithery MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Smithery 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 Smithery to LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.