Does order placement via the API bypass the normal Rappi checkout security rules?

No. The `place_order` tool uses your pre-authenticated method securely. Bank validations, anti-fraud algorithms, and location checks apply exactly as they would natively in the application avoiding compliance breaches entirely.

Can I explicitly track multiple concurrent active orders dynamically simultaneously?

Yes! Your agent can iterate over an array of active Order IDs using the `get_order_status` tool consistently. It parses coordinate updates and ETA drops, outputting a consolidated table summing up your entire inbound fleet seamlessly.

Are the store identifiers (storeId) universally stable across regions?

Store IDs are fundamentally unique keys generated explicitly by Rappi's catalog backend. They remain static for the lifetime of that specific branch location. Searching via `list_nearby_stores` initially is recommended to cache the correct target IDs dynamically.

Rappi API MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Rappi API
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Rappi API through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "rappi-api": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Rappi API, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Rappi API MCP Server

Empower your intelligent agents directly with Rappi API, the dominant delivery and logistics super-app bridging Latin America. Bypass chaotic consumer screens and deploy 10 robust tools natively automating restaurant queries, massive localized delivery management, and real-time courier tracking through any AI system magically.

LangChain's ecosystem of 500+ components combines seamlessly with Rappi API through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Logistics Control — Trace geographic rider coordinates, predict dynamic delivery fees, and seamlessly tip couriers natively without touching mobile interfaces.
Order Placement — Query menu endpoints securely, push nested orders to specific merchant ID endpoints, and execute transactions automatically in the background.
Customer Care Automation — Dispute missing items securely opening help tickets and communicating automatically via Support endpoints.
Market Analysis — Poll local 'Turbo' supermarkets or pharmacies parsing active stock pricing to feed comparative database dashboards.

The Rappi API MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 Rappi API to LangChain via MCP

Follow these steps to integrate the Rappi API MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Rappi API via MCP

Why Use LangChain with the Rappi API MCP Server

LangChain provides unique advantages when paired with Rappi API through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Rappi API MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Rappi API queries for multi-turn workflows

Rappi API + LangChain Use Cases

Practical scenarios where LangChain combined with the Rappi API MCP Server delivers measurable value.

RAG with live data: combine Rappi API tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Rappi API, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Rappi API tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Rappi API tool call, measure latency, and optimize your agent's performance

Rappi API MCP Tools for LangChain (10)

These 10 tools become available when you connect Rappi API to LangChain via MCP:

get_order_detail

Get full details for a specific order

get_order_handoff

Get handoff confirmation codes for an order

get_store_availability

Check availability status of a store

get_store_menu

Retrieve the full menu of a store

list_new_orders

List new incoming orders awaiting acceptance

list_stores

List all registered stores under your account

mark_ready_for_pickup

Signal that an order is ready for courier pickup

reject_order

Reject an incoming order with a reason

take_order

Accept and start preparing an incoming order

update_store_status

Open or close a store for receiving orders

Example Prompts for Rappi API in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Rappi API immediately.

"List nearby stores in the 'pharmacy' category around coordinates 4.6097, -74.0817."

"Check the delivery state and ETA for my active order number 8812920."

"Cancel the active order 88910 immediately citing missing items from the receipt."

Troubleshooting Rappi API MCP Server with LangChain

Common issues when connecting Rappi API to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Rappi API + LangChain FAQ

Common questions about integrating Rappi API MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.