How to Use the Cedar AI MCP in LlamaIndex

Q: Can I filter Cedar AI tools for LlamaIndex?

You can use an allowedtools filter to restrict which operations the agent can perform. This prevents the model from modifying data when you only need read access.

Index live rail yard data into LlamaIndex using Cedar AI to get grounded answers about your fleet.

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Connect Cedar AI MCP to LlamaIndex

Create your Vinkius account to connect Cedar 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 Cedar AI with LlamaIndex

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Index Railcar Intelligence

Call `get_railcar_details` and feed the output into your LlamaIndex vector store. This creates a searchable knowledge base of your current fleet status. Your RAG application now grounds answers in live API data. Instead of guessing, your system retrieves the exact configuration of railcars in the yard.

Searchable Work Orders

Use `list_work_orders` to populate your index with active operational requirements. You query your documents and get results that include current work order status. This approach eliminates hallucinations by pinning the agent to the output of `get_work_order_details`. Your users get accurate reports based on the latest yard events.

Track Waybill History

Index the results of `list_waybills` to track document history over time. You compare past waybill states against current yard arrivals. Your system treats the MCP tool output as a first-class data source. You perform semantic searches across your freight records to identify bottlenecks.

Setup guide

Set up Cedar 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 Cedar 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 Cedar AI tools.",
)
response = await agent.run("List recent Cedar AI data")

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Common questions about Cedar AI MCP in LlamaIndex

Use the McpToolSpec to fetch the tool definitions. The client then transforms the output into objects that your vector store can digest.

It does. You pass the converted tool list to your FunctionAgent to allow the model to query live rail data during the retrieval process.

You can use an allowed_tools filter to restrict which operations the agent can perform. This prevents the model from modifying data when you only need read access.

You trigger a refresh of your index by calling the tools as part of your ingestion pipeline. This ensures your knowledge base matches the current rail yard status.

We enforce strict isolation for all waybill information. Your data remains within your authorized scope and is never shared across other user sessions.

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