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

Q: How do I load Câmara dos Deputados (v2) tools into my AutoGen agents?

Install autogen-extmcp. Use mcpservertools with StreamableHttpServerParams to fetch the endpoints, then pass that list directly into your AssistantAgent constructor.

Q: Does Câmara dos Deputados (v2) work with AutoGen's streaming transport?

The integration fully supports StreamableHttpServerParams. This keeps the connection open while your agents deliberate over large datasets like historical expense reports.

Give your AutoGen multi-agent teams direct access to Brazilian Chamber of Deputies data.

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Connect Câmara dos Deputados (v2) MCP to AutoGen

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Setup Câmara dos Deputados (v2) with AutoGen

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Let AutoGen agents debate legislative data

The `list_proposicoes` tool gives your AutoGen agents raw access to pending Brazilian bills so they can analyze political impact. You set up a system where one agent fetches the bill text, while a second agent challenges the first's interpretation of the legal phrasing. This multi-agent setup thrives on conflicting perspectives. A financial agent might pull `list_deputado_despesas` to audit a politician's spending, while a political strategy agent uses `list_deputado_eventos` to argue that the travel costs were justified by public appearances.

Analyze voting patterns through consensus

Tracking party loyalty requires combining `list_votacao_votos` with `list_votacao_orientacoes`. Your data-gathering agent pulls the individual votes and the party leadership recommendations, feeding both into the group chat. The agents then debate whether a specific deputy is breaking party lines. Because the MCP Server handles all the schema conversions automatically, your agents spend their context window deliberating the actual political data instead of formatting JSON.

Map out parliamentary alliances

Finding out who actually controls a committee starts with `list_orgao_membros` and `list_frente_membros`. One agent maps the official committee structure while another cross-references those names against informal parliamentary fronts. They negotiate a final report on political influence. If they spot a gap in their reasoning, one of them will independently decide to call `get_partido` to pull historical affiliation data and settle the argument.

Setup guide

Set up Câmara dos Deputados (v2) MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Câmara dos Deputados (v2) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Câmara dos Deputados (v2)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Câmara dos Deputados (v2) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Câmara dos Deputados (v2)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Câmara dos Deputados (v2) data")
    print(result.messages[-1].content)

Get your connection token →

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 AutoGen

Install `autogen-ext[mcp]`. Use `mcp_server_tools` with `StreamableHttpServerParams` to fetch the endpoints, then pass that list directly into your `AssistantAgent` constructor.

Yes. You typically assign the MCP tool execution capability to a specific data-gathering agent, which then shares the fetched voting records and transcripts with the rest of the chat group.

The integration fully supports `StreamableHttpServerParams`. This keeps the connection open while your agents deliberate over large datasets like historical expense reports.

They can fetch committee meetings, public hearings, and floor sessions using `list_eventos`. They can also see exactly which politicians attended by calling `list_evento_deputados`.

The agents only access publicly disclosed Parliamentary Quota receipts and official voting records. Vinkius enforces a zero-trust architecture, ensuring that the internal debates between your agents never leak out to the external endpoint.

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