AfterShip Returns MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add AfterShip Returns as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to AfterShip Returns. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in AfterShip Returns?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About AfterShip Returns MCP Server
Connect your AfterShip Returns account to your AI agent to unlock professional returns management and customer experience orchestration. From auditing pending return requests to approving RMAs and generating shipping labels, your agent handles your reverse logistics through natural conversation.
LlamaIndex agents combine AfterShip Returns tool responses with indexed documents for comprehensive, grounded answers. Connect 4 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Return Request Management — List and audit return requests from customers and check their approval status
- RMA Orchestration — Retrieve detailed technical metadata for specific RMAs, including item details and reasons for return
- Label Generation Support — Monitor shipment creation and retrieve tracking information for return packages
- Logistics Oversight — Mark items as received and grade their condition to streamline your warehouse workflow
- Process Insights — Quickly identify common return reasons or identify bottlenecks in your return policy directly from chat
The AfterShip Returns MCP Server exposes 4 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect AfterShip Returns to LlamaIndex via MCP
Follow these steps to integrate the AfterShip Returns MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 4 tools from AfterShip Returns
Why Use LlamaIndex with the AfterShip Returns MCP Server
LlamaIndex provides unique advantages when paired with AfterShip Returns through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine AfterShip Returns tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain AfterShip Returns tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query AfterShip Returns, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what AfterShip Returns tools were called, what data was returned, and how it influenced the final answer
AfterShip Returns + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the AfterShip Returns MCP Server delivers measurable value.
Hybrid search: combine AfterShip Returns real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query AfterShip Returns to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying AfterShip Returns for fresh data
Analytical workflows: chain AfterShip Returns queries with LlamaIndex's data connectors to build multi-source analytical reports
AfterShip Returns MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect AfterShip Returns to LlamaIndex via MCP:
approve_return
This allows the customer to ship the item back. Authorize a pending return request to immediately trigger generating the return shipping label
get_return_details
Retrieve the granular items, return reasons, and current logistics status for a specific RMA
list_returns
Retrieve pending or historical customer return requests and their processing statuses
receive_items
Record the arrival and physical grading condition of returned items arriving at the warehouse
Example Prompts for AfterShip Returns in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with AfterShip Returns immediately.
"List all pending return requests from the last 48 hours."
"Approve return request ID 'ret_abc123'."
"Show me details for RMA number 'RMA-98765'."
Troubleshooting AfterShip Returns MCP Server with LlamaIndex
Common issues when connecting AfterShip Returns to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAfterShip Returns + LlamaIndex FAQ
Common questions about integrating AfterShip Returns MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect AfterShip Returns with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect AfterShip Returns to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.