Cradl AI MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cradl AI as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cradl AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Cradl AI?"
)
print(response)
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.
LlamaIndex agents combine Cradl AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Cradl AI MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Cradl AI
Why Use LlamaIndex with the Cradl AI MCP Server
LlamaIndex provides unique advantages when paired with Cradl AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cradl AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cradl AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cradl AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cradl AI tools were called, what data was returned, and how it influenced the final answer
Cradl AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cradl AI MCP Server delivers measurable value.
Hybrid search: combine Cradl AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cradl AI to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cradl AI for fresh data
Analytical workflows: chain Cradl AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Cradl AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Cradl AI to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Cradl AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCradl AI + LlamaIndex FAQ
Common questions about integrating Cradl AI MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP 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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.