Cradl AI MCP Server for AutoGen 10 tools — connect in under 2 minutes
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Cradl AI as an MCP tool provider through the Vinkius and every agent in the group can access live data and take action.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="cradl_ai_agent",
tools=tools,
system_message=(
"You help users with Cradl AI. "
"10 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
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.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Cradl AI tools. Connect 10 tools through the Vinkius and assign role-based access — a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
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 AutoGen 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 AutoGen via MCP
Follow these steps to integrate the Cradl AI MCP Server with AutoGen.
Install AutoGen
Run pip install "autogen-ext[mcp]"
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Integrate into workflow
Use the agent in your AutoGen multi-agent orchestration
Explore tools
The workbench discovers 10 tools from Cradl AI automatically
Why Use AutoGen with the Cradl AI MCP Server
AutoGen provides unique advantages when paired with Cradl AI through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Cradl AI tools to solve complex tasks
Role-based architecture lets you assign Cradl AI tool access to specific agents — a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Cradl AI tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Cradl AI tool responses in an isolated environment
Cradl AI + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Cradl AI MCP Server delivers measurable value.
Collaborative analysis: one agent queries Cradl AI while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Cradl AI, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Cradl AI data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Cradl AI responses in a sandboxed execution environment
Cradl AI MCP Tools for AutoGen (10)
These 10 tools become available when you connect Cradl AI to AutoGen 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 AutoGen
Ready-to-use prompts you can give your AutoGen 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 AutoGen
Common issues when connecting Cradl AI to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Cradl AI + AutoGen FAQ
Common questions about integrating Cradl AI MCP Server with AutoGen.
How does AutoGen connect to MCP servers?
Can different agents have different MCP tool access?
Does AutoGen support human approval for tool calls?
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 AutoGen
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.