How to Use the DPD MCP in LlamaIndex

Q: How do I index DPD shipping histories in LlamaIndex?

You use listshipments to retrieve your recent orders and load them into a Document object. LlamaIndex then parses these records, making your DPD shipment history fully searchable via vector queries.

Q: Can LlamaIndex agents generate DPD labels?

Yes, the agent can call createshipment to initiate the order and then fetch the physical shipping label using getlabels.

Q: How does the LlamaIndex tool spec handle DPD API errors?

If a tool like getparcelstatus fails, the MCP Server returns a structured error payload. LlamaIndex captures this exception, allowing the agent to attempt a fallback query or alert the operator.

Q: Can I use LlamaIndex to find DPD pickup locations?

The agent can call findparcelshop with latitude and longitude coordinates. LlamaIndex indexes these physical locations so your application can recommend the closest pickup point.

Index live DPD logistics data and tracking histories directly into your LlamaIndex knowledge bases.

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Create your Vinkius account to connect DPD 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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Ground logistics queries in live DPD data with LlamaIndex

This MCP server connects your LlamaIndex RAG pipelines directly to DPD shipping databases. Your agent calls `list_shipments` to pull recent orders, then indexes the shipment states so your queries are grounded in real-time shipping records. Instead of relying on static database exports, the agent fetches fresh status updates using `get_shipment_status`. This live context is injected into your vector store, ensuring your support bots never hallucinate delivery dates.

Synthesize physical manifest reports

This shipping toolset lets your agent use `get_manifest` to retrieve dispatch documents and parse their contents into searchable index nodes. It combines this physical dispatch data with product lists retrieved via `list_products` to build a unified logistics knowledge base. This structure allows you to run semantic searches over physical cargo loads. Users can ask natural language questions about dispatch volumes, and the agent retrieves the exact match from the indexed manifest files.

Context-aware parcel shop routing

Your agent queries `find_parcelshop` to locate pickup points based on the user's geographic coordinates. The resulting location data is indexed alongside `list_countries` data to verify international delivery eligibility. By indexing these endpoints, your application matches a customer's location to the nearest valid DPD depot. The agent uses this semantic mapping to prepare the payload for `create_shipment` without manual lookup tables.

Setup guide

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

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Common questions about DPD MCP in LlamaIndex

You use `list_shipments` to retrieve your recent orders and load them into a Document object. LlamaIndex then parses these records, making your DPD shipment history fully searchable via vector queries.

Yes, the agent can call `create_shipment` to initiate the order and then fetch the physical shipping label using `get_labels`.

If a tool like `get_parcel_status` fails, the MCP Server returns a structured error payload. LlamaIndex captures this exception, allowing the agent to attempt a fallback query or alert the operator.

The agent can call `find_parcelshop` with latitude and longitude coordinates. LlamaIndex indexes these physical locations so your application can recommend the closest pickup point.

No, the MCP Server only acts as a secure bridge for your tracking numbers and address details. Your data flows directly to your LlamaIndex vector store without being cached on our servers.

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