Alpic MCP Server for LangChain 18 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Alpic through 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({
"alpic": {
"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 Alpic, 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 Alpic MCP Server
What you can do
Connect AI agents to the Alpic platform for complete MCP server lifecycle management:
LangChain's ecosystem of 500+ components combines seamlessly with Alpic through native MCP adapters. Connect 18 tools via 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.
- List and manage teams with member access controls
- Create, update, and delete MCP server projects with git repository linking
- Deploy to multiple environments (dev, staging, production) with one command
- Monitor deployments with real-time status, logs, and analytics
- Manage environment variables securely for each deployment target
- View analytics including request counts, latency, error rates, and usage patterns
- Publish to the MCP registry to make your servers discoverable
- Create development tunnels for local testing before production deployment
The Alpic MCP Server exposes 18 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 Alpic to LangChain via MCP
Follow these steps to integrate the Alpic 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 18 tools from Alpic via MCP
Why Use LangChain with the Alpic MCP Server
LangChain provides unique advantages when paired with Alpic through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Alpic 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 Alpic queries for multi-turn workflows
Alpic + LangChain Use Cases
Practical scenarios where LangChain combined with the Alpic MCP Server delivers measurable value.
RAG with live data: combine Alpic tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Alpic, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Alpic tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Alpic tool call, measure latency, and optimize your agent's performance
Alpic MCP Tools for LangChain (18)
These 18 tools become available when you connect Alpic to LangChain via MCP:
add_variable
Use this to set API keys, database URLs, feature flags, or any configuration needed by your MCP server. Requires project ID, environment ID, variable key, and value. Variable values are stored securely. Add a new environment variable to an Alpic environment
create_environment
Requires environment name and project ID. Optionally set initial variables and configuration. Each environment gets a unique URL for MCP client connections. Returns the created environment details. Create a new deployment environment (dev, staging, prod) for an Alpic project
create_project
Requires project name and team ID. Optionally set description, repository URL, and initial configuration. Returns the created project details including the new project ID needed for subsequent operations. Create a new MCP server project in Alpic
delete_project
This action cannot be undone. Use with caution. Requires the project ID. Confirm with the user before proceeding. Delete an Alpic MCP server project
delete_variable
Use this to clean up unused configuration keys. Requires project ID, environment ID, and variable key. Delete an environment variable from an Alpic environment
deploy_environment
The deployment runs asynchronously. Returns the deployment ID which can be used with get_deployment to check status. Use this to push new MCP server versions to dev, staging, or production environments. Trigger a new deployment for a specific Alpic environment
get_deployment
Requires the deployment ID. Use this to check if a deployment succeeded, review deployment history, or debug failed deployments. Get detailed status and metadata for a specific Alpic deployment
get_deployment_logs
Useful for debugging failed deployments, understanding build output, or verifying successful startup of the MCP server. Requires project ID and environment ID. Get deployment logs for a specific Alpic environment
get_project
Requires the project ID from list_projects results. Use this to review project settings before making updates or triggering deployments. Get detailed information about a specific Alpic MCP server project
get_project_analytics
Requires the project ID. Use this to monitor MCP server health, identify performance trends, and troubleshoot issues. Get analytics and usage data for a specific Alpic project
get_server_info
Use this to verify which MCP tools are exposed and confirm the server is running correctly. Get server information and status for a specific Alpic project
get_tunnel_ticket
Returns the tunnel URL and ticket token. Use this during development to test your MCP server before deploying to a production environment. Get a tunnel ticket for local development and testing of an MCP server
list_environments
Each environment has its own URL, variables, and deployment status. Returns environment IDs, names, URLs, and current deployment state. Use this to identify which environment to deploy to or manage variables for. List all environments (dev, staging, prod) for a specific Alpic project
list_projects
Returns project IDs, names, descriptions, associated teams, deployment status, and environment counts. Use this to overview your entire MCP infrastructure before managing specific projects or triggering deployments. List all MCP server projects in your Alpic account
list_teams
Each team contains projects and environments for deploying MCP servers. Returns team IDs, names, and member counts. Use this first to identify which team to manage projects under. List all teams associated with your Alpic account
list_variables
Variable values are masked for security. Returns variable keys and metadata. Use this to audit environment configuration before deploying or adding new variables. List all environment variables configured for an Alpic environment
publish_to_registry
Requires project ID and optionally a server description and category. Use this to make your MCP server publicly available. Publish an MCP server to the official MCP registry via Alpic
update_project
Only pass the fields you want to change. Requires the project ID from list_projects results. Use this to rename projects, update descriptions, or point to a new repository branch. Update an existing Alpic MCP server project configuration
Example Prompts for Alpic in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Alpic immediately.
"List all active Alpic projects running on my account natively, then check the error rate metric for the first one listed."
"Deploy the staging environment for our main enterprise project mapped on isolated branches."
"Audit the credentials in our production environment. Provide exact details of variable schemas missing from active lists."
Troubleshooting Alpic MCP Server with LangChain
Common issues when connecting Alpic to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAlpic + LangChain FAQ
Common questions about integrating Alpic 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 Alpic 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 Alpic to LangChain
Get your token, paste the configuration, and start using 18 tools in under 2 minutes. No API key management needed.