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How to Use the FedEx MCP in LlamaIndex

Index live FedEx shipping data into your LlamaIndex knowledge base for semantic search and grounded agent responses.

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LlamaIndex

Connect FedEx MCP to LlamaIndex

Create your Vinkius account to connect FedEx 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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Query FedEx logistics with LlamaIndex

Turn `get_proof_of_delivery` results into searchable documents within your index. You can ask questions about past deliveries using natural language. Retrieve historical shipping data to find patterns in your logistics. LlamaIndex stores these results for instant access.

Find locations using LlamaIndex

Call `find_locations` to pull address and hour data for local drop-off points. Your agent adds this to its context for better decision-making. Filter results by service type to find the right office. The index keeps this information ready for your next query.

Validate shipments with LlamaIndex

Use `validate_address` to confirm recipient details and store the outcome. This ensures your knowledge base contains only accurate, verified addresses. Check `get_postal_code` results to ground your agent in reality. It prevents hallucinated shipping routes during your RAG process.

Setup guide

Set up FedEx MCP in LlamaIndex

Prerequisites

  • Python 3.10+ installed
  • llama-index-tools-mcp package
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

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

  2. 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. 3

    Convert to LlamaIndex tools

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

  4. 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 FedEx tools.",
)
response = await agent.run("List recent FedEx data")

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FedEx. 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 FedEx MCP in LlamaIndex

Use the MCP tool spec to convert server functions into a format the agent understands. This allows your RAG application to pull live data into the index.
You can trigger `track_package` to fetch the latest status and index that result. This keeps your agent updated with current information.
Your package information stays within your local environment. The server acts as a secure bridge, and you control how this information is indexed.
By indexing tool outputs, you create a searchable record of your logistics activity. You can find specific shipments by querying the content of the data.
This server only touches tracking numbers, delivery status, and address details. Your security configuration determines exactly what reaches your vector storage.

Start using the FedEx MCP today

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