How to Use the Correios (Brazil) MCP in LlamaIndex
Query and index real-time Brazilian shipping data in LlamaIndex using the Correios MCP Server.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Correios (Brazil) MCP to LlamaIndex
Create your Vinkius account to connect Correios (Brazil) 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.
Index Brazilian postal data for LlamaIndex RAG
`lookup_cep` retrieves structured Brazilian address details that your LlamaIndex pipeline can index directly into a vector store. Instead of just showing the raw address to the user, your agent indexes the street, neighborhood, and city details to build a searchable database of customer locations. This lets you build local search applications over MCP without maintaining an offline copy of the Brazilian postal database. Your agent queries this index to find patterns in shipping destinations or to auto-complete addresses for returning users.
Build searchable logs of Correios package tracking
`track_object` fetches live tracking events from the Brazilian postal service, which LlamaIndex then indexes as document nodes. When customers ask about their shipment history, your RAG system queries these indexed nodes instead of hitting the live Correios API repeatedly. This reduces API load and speeds up response times. Your agent can search past tracking updates to identify recurring transit bottlenecks or calculate average delivery times for specific regions.
Ground shipping cost estimates in real-time contract data
`calculate_price_deadline` provides live shipping quotes that your LlamaIndex agent combines with static product catalog documents to generate accurate shipping quotes. The agent retrieves dimensions from your product index, feeds them to the Correios tool, and returns a grounded estimate. This eliminates hallucinations about shipping fees and delivery times. By using `list_contract_services`, the agent also indexes your negotiated corporate rates, ensuring quotes always reflect your actual contract pricing.
Set up Correios (Brazil) 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 Correios (Brazil) 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 Correios (Brazil) tools.",
)
response = await agent.run("List recent Correios (Brazil) data") Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Correios. 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 Correios (Brazil) MCP in LlamaIndex
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