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.
Works with every AI agent you already use
…and any MCP-compatible client
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.
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.
Set up FedEx MCP in LlamaIndex
Prerequisites
- Python 3.10+ installed
-
llama-index-tools-mcppackage - 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 providesBasicMCPClientandMcpToolSpec. - 2
Connect with BasicMCPClient
Point
BasicMCPClientto 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 FedEx MCP tools into nativeFunctionToolobjects that any LlamaIndex agent can use. - 4
Run with any LLM
Create a
FunctionAgentwith the tools and your preferred LLM. SwapOpenAIforAnthropic,Gemini, or any LlamaIndex-supported provider.
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
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