How to Use the ALESP (Assembleia SP) MCP in LangChain

Q: Can I use LangChain to track ALESP (Assembleia SP) committee votes?

Yes, pass getcomissoesvotacoes to your agent tools. The agent will then have the ability to query specific committee voting records on demand.

Build complex legislative reasoning chains for LangChain using real-time data from the ALESP (Assembleia SP) MCP Server.

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Connect ALESP (Assembleia SP) MCP to LangChain

Create your Vinkius account to connect ALESP (Assembleia SP) 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 ALESP (Assembleia SP) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain legislative data in LangChain

Feed deputy expense data directly into your decision logic. You can use `get_despesas_gabinetes` to pull raw numbers and pipe them into an analytical chain for immediate validation. Your agent decides which tool to trigger based on the previous output. By chaining `get_deputados` with specific committee queries, you map out the entire power structure of the house.

Trace ALESP (Assembleia SP) tool calls

Monitor every interaction between your agent and the assembly database through LangSmith. You see exactly how `get_documento_andamento` informs the final legislative summary without guessing. Latency and token usage are visible for every step. This makes debugging your agent's reasoning process significantly easier when dealing with hundreds of legislative records.

Automate multi-step legislative research

Construct agents that perform deep dives into state law. Your agent might start by calling `get_legislacao_normas` and then drill down into specific themes to build a comprehensive report. Connecting this MCP Server to your graph allows for persistent memory across complex sessions. You aren't just fetching data; you're building a structured knowledge pipeline.

Setup guide

Set up ALESP (Assembleia SP) 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 ALESP (Assembleia SP) 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({
    "alesp-assembleia-sp-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 ALESP (Assembleia SP) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about ALESP (Assembleia SP) MCP in LangChain

Install the necessary adapters and initialize the server with your endpoint. Once connected, your agent pulls the tool definitions directly from the registry.

Yes, pass `get_comissoes_votacoes` to your agent tools. The agent will then have the ability to query specific committee voting records on demand.

You can wrap your client in a session to maintain context. This ensures your agent remembers previous legislative queries throughout the conversation.

The framework catches the error and reports it to your trace. You can then define fallback logic to retry or alert the user.

Your request data remains isolated within your infrastructure. The server only processes legislative public records, keeping your specific agent's research queries strictly local.

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