How to Use the Câmara dos Deputados (v2) MCP in LangChain

Q: How do I connect Câmara dos Deputados (v2) to my LangChain agent?

Install langchain-mcp-adapters. Then initialize MultiServerMCPClient pointing to the server URL and pass client.gettools() directly to your ReAct agent constructor.

Q: Does Câmara dos Deputados (v2) support LangChain memory?

The MCP tools themselves are stateless. You need to use client.session() or attach standard memory modules to your agent to remember which deputy you queried earlier.

Build multi-step ReAct agents in LangChain that query Brazilian legislative data and track deputy expenses.

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Connect Câmara dos Deputados (v2) MCP to LangChain

Create your Vinkius account to connect Câmara dos Deputados (v2) to LangChain 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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Setup Câmara dos Deputados (v2) with LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain LangChain agents to Brazilian legislative data

The `list_deputados` tool feeds your LangChain ReAct agents live profiles of active Brazilian politicians. You build pipelines where one step grabs a specific politician using `get_deputado` and the next pulls their exact reimbursement records through `list_deputado_despesas`. Every call gets traced in LangSmith so you see exactly how many tokens the agent burned parsing public spending. Your agent decides the execution order, maybe pulling `list_proposicao_autores` to find bill sponsors before checking their recent speeches with `list_deputado_discursos`.

Track propositions and voting records

Finding out how a bill moved through the Chamber starts with the `get_proposicao` tool. Your code passes the bill ID, grabs the status, and immediately pipes that context into `list_proposicao_tramitacoes` to build a complete timeline of legislative progress. Then you tie in the actual votes. By calling `list_votacao_votos`, the agent pulls individual nominal votes cast by deputies on that specific bill. This MCP Server turns raw government XML feeds into clean JSON that your reasoning pipelines digest without writing custom parsers.

Map political alliances and party structures

Understanding who sits where requires hitting the `list_partidos` and `get_frente` tools. Your pipeline can pull every member of a parliamentary front using `list_frente_membros` and cross-reference them against their party affiliations. ReAct agents excel at this kind of multi-hop reasoning. They ask for the committee members via `list_orgao_membros`, notice a specific deputy, and spontaneously decide to check their voting recommendations using `list_votacao_orientacoes`.

Setup guide

Set up Câmara dos Deputados (v2) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Câmara dos Deputados (v2) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

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

async with MultiServerMCPClient({
    "camara-dos-deputados-v2-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Câmara dos Deputados (v2) transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Câmara dos Deputados. 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 Câmara dos Deputados (v2) MCP in LangChain

Install `langchain-mcp-adapters`. Then initialize `MultiServerMCPClient` pointing to the server URL and pass `client.get_tools()` directly to your ReAct agent constructor.

Yes, LangSmith automatically traces every tool invocation. You see the exact inputs sent to `list_deputado_despesas` and the latency of the API response.

The MCP tools themselves are stateless. You need to use `client.session()` or attach standard memory modules to your agent to remember which deputy you queried earlier.

You get direct access to the Parliamentary Quota (CEAP) reimbursements. The `list_despesas_cota` tool lets your code search across multiple deputies for specific spending categories.

Your application only reads public government records like speeches, votes, and expense receipts. Vinkius runs the integration inside a V8 Isolate Sandbox, ensuring your pipeline never leaks internal agent state to the external endpoint.

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