Cradl AI MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Cradl AI 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({
"cradl-ai": {
"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 Cradl AI, 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 Cradl AI MCP Server
Integrate Cradl AI, the advanced document data extraction platform, directly into your AI workflow. Automate the processing of invoices, receipts, IDs, and custom forms using powerful deep learning models and natural language.
LangChain's ecosystem of 500+ components combines seamlessly with Cradl AI through native MCP adapters. Connect 10 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.
What you can do
- Data Extraction — Trigger real-time data extraction from document URLs with high precision.
- Model Management — List and explore your custom-trained extraction models.
- Workflow Monitoring — Track the status of document processing flows and individual tasks.
- Batch Processing — Audit and retrieve details for entire batches of processed documents.
The Cradl AI MCP Server exposes 10 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 Cradl AI to LangChain via MCP
Follow these steps to integrate the Cradl AI 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 10 tools from Cradl AI via MCP
Why Use LangChain with the Cradl AI MCP Server
LangChain provides unique advantages when paired with Cradl AI through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Cradl AI 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 Cradl AI queries for multi-turn workflows
Cradl AI + LangChain Use Cases
Practical scenarios where LangChain combined with the Cradl AI MCP Server delivers measurable value.
RAG with live data: combine Cradl AI tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Cradl AI, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Cradl AI tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Cradl AI tool call, measure latency, and optimize your agent's performance
Cradl AI MCP Tools for LangChain (10)
These 10 tools become available when you connect Cradl AI to LangChain via MCP:
extract_data_from_url
Touches OCR engine, model prediction, and data normalization boundary. Trigger a new data extraction prediction from a file URL
get_batch_details
Touches individual file statuses and batch-level processing summary boundaries. Get details for a specific batch of documents
get_flow_details
Touches integration points and document routing rules boundaries. Get structure and settings for a specific flow
get_model_details
Touches schema definitions, extraction accuracy metrics, and model metadata boundaries. Get details for a specific extraction model
get_task_status
Resolves confidence scores and extracted key-value pairs from the document. Check the status and results of a document task
list_batches
Resolves batch identifiers, creation dates, and total document counts within each batch. List all document batches
list_extraction_models
Resolves model names, versions, and training statuses for document analysis. List all data extraction models in Cradl AI
list_processing_tasks
Resolves task IDs, statuses (PENDING, COMPLETED, FAILED), and processing timestamps. List recent document processing tasks
list_workflows
Resolves flow IDs, triggers, and configured processing steps. List all document processing flows
search_models_by_name
Resolves model metadata based on a name keyword search. Search for extraction models by name
Example Prompts for Cradl AI in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Cradl AI immediately.
"Extract data from this invoice: https://example.com/inv123.pdf using my 'Invoice Parser' model."
"Check the status of document processing task 't8s9df7'."
"List all extraction models available in my account."
Troubleshooting Cradl AI MCP Server with LangChain
Common issues when connecting Cradl AI to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersCradl AI + LangChain FAQ
Common questions about integrating Cradl AI 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 Cradl AI 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 Cradl AI to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.