How to Use the Cradl AI MCP in LlamaIndex

Q: How do I connect Cradl AI to LlamaIndex?

Install llama-index-tools-mcp. Initialize a BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex search through Cradl AI flow configurations?

Your agent pulls routing rules via getflowdetails and embeds them. You can then ask questions about how specific documents are routed.

Q: How does LlamaIndex handle Cradl AI extraction results?

The agent monitors gettaskstatus until completion. Once the key-value pairs return, they are indexed into your vector store for semantic querying.

Q: Can I filter which extraction tools the agent sees?

Use the allowedtools filter when setting up the MCP tool spec. You might only expose read-only tools like listworkflows to certain agents.

Index extracted document data directly into your LlamaIndex vector store using the Cradl AI MCP server.

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Connect Cradl AI MCP to LlamaIndex

Create your Vinkius account to connect Cradl AI to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cradl AI with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index OCR data with this MCP Server

Function agents pull structured data and immediately embed it. You trigger `extract_data_from_url` on a stack of receipts, and the agent catches the parsed JSON. That structured data gets chunked and indexed. Users then query your RAG application to find specific vendor names or totals grounded in the actual document payloads.

Query live extraction models

Your knowledge base does not have to be static. By giving LlamaIndex access to `list_extraction_models`, your RAG app knows exactly which schemas are currently active. Ask your agent about model accuracy. It calls `get_model_details`, reads the extraction accuracy metrics, and returns an answer based on live API state rather than outdated training data.

Build searchable batch histories

Tracking historical document processing is usually a pain. Your agent runs `list_batches` and `get_batch_details` to build a localized index of processing summaries. Semantic search handles the rest. When a user asks what happened to last week's invoices, the agent queries the vector store containing those exact batch statuses.

Setup guide

Set up Cradl AI MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cradl AI MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cradl AI tools.",
)
response = await agent.run("List recent Cradl AI data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cradl AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Cradl AI MCP in LlamaIndex

Install `llama-index-tools-mcp`. Initialize a `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and pass the async tool list to your FunctionAgent.

Your agent pulls routing rules via `get_flow_details` and embeds them. You can then ask questions about how specific documents are routed.

The agent monitors `get_task_status` until completion. Once the key-value pairs return, they are indexed into your vector store for semantic querying.

Use the `allowed_tools` filter when setting up the MCP tool spec. You might only expose read-only tools like `list_workflows` to certain agents.

Extracted key-value pairs and file URLs route through an ephemeral Vinkius sandbox. The connection is zero-trust, meaning your sensitive invoice data never hits a persistent disk.

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