Indy MCP Server for LangChainGive LangChain instant access to 12 tools to Create Form, Create Webhook, Delete Form, and more
LangChain is the leading Python framework for composable LLM applications. Connect Indy 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 App Connector for LangChain
The Indy app connector for LangChain is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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({
"indy": {
"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 Indy, 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 Indy MCP Server
Connect your Indy account to any AI agent and manage forms and records through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Indy through native MCP adapters. Connect 12 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.
What you can do
- Form Management — List all forms, inspect configurations, create new forms, and delete unused ones
- Record Tracking — Browse all form submissions, inspect individual records with full field data
- Template Management — List and inspect form templates for reusable designs
- Group Organization — Browse form groups for organized management
- File Access — List files attached to form submissions
The Indy MCP Server exposes 12 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.
All 12 Indy tools available for LangChain
When LangChain connects to Indy through Vinkius, your AI agent gets direct access to every tool listed below — spanning form-builder, data-collection, freelance-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new form
Create a new webhook
Delete a form
Delete a webhook
Get account status
Get form details
Get submission details
Get user details
List all forms
List form submissions
List connected users
List active webhooks
Connect Indy to LangChain via MCP
Follow these steps to wire Indy into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Indy MCP Server
LangChain provides unique advantages when paired with Indy through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Indy 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 Indy queries for multi-turn workflows
Indy + LangChain Use Cases
Practical scenarios where LangChain combined with the Indy MCP Server delivers measurable value.
RAG with live data: combine Indy tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Indy, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Indy tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Indy tool call, measure latency, and optimize your agent's performance
Example Prompts for Indy in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Indy immediately.
"Show all forms and the latest submissions for the 'Customer Feedback' form."
"Create a new 'Event Registration' form and list available templates."
"Show all records for the bug report form and any attached files."
Troubleshooting Indy MCP Server with LangChain
Common issues when connecting Indy to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersIndy + LangChain FAQ
Common questions about integrating Indy 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.