How to Use the Brazilian Judiciary Dictionary Engine MCP in LangChain

Build chains that pinpoint Brazilian courts accurately using the Brazilian Judiciary Dictionary Engine within your LangChain pipeline.

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Connect Brazilian Judiciary Dictionary Engine MCP to LangChain

Create your Vinkius account to connect Brazilian Judiciary Dictionary Engine 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 Brazilian Judiciary Dictionary Engine with LangChain

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Trace legal entity detection in LangChain

Feed your legal documents into a chain and use `search_legal_entities` to extract tribunal mentions. Every match gets logged in LangSmith, so you can debug the exact input text and entity output. This MCP Server handles the lookup locally. You see the full trace of how the agent identifies specific regulatory bodies without guessing.

Chain legal lookups with other LangChain tools

Connect the dictionary output to your database tools. If `search_legal_entities` finds a court name, pass that string directly to a SQL query in the next step of your chain. Developers use this to build multi-step reasoning. The agent pulls the entity, verifies it against your database, and generates a report.

Stateful sessions for complex legal analysis

Maintain context across multiple document chunks using the client session. You don't lose track of which entities appeared in which paragraph. Keep the state clean while the agent processes long legal filings. It keeps the workflow moving without re-scanning the entire dictionary.

Setup guide

Set up Brazilian Judiciary Dictionary Engine 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 Brazilian Judiciary Dictionary Engine 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({
    "brazilian-judiciary-dictionary-engine-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 Brazilian Judiciary Dictionary Engine transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Brazilian Judiciary Dictionary Engine MCP in LangChain

Install the adapters and pass your server URL to the client. Once the client loads, use the tool list in your agent constructor.

Yes. It processes text chunks efficiently through the tool interface. You feed the document segments into the chain one by one.

It does. You can aggregate the Brazilian Judiciary Dictionary Engine alongside your other data sources in one LangGraph runtime.

The server runs the matching logic locally. No sensitive data leaves your environment during the dictionary lookup process.

Your raw text stays in memory. The tool only reads the string to identify court names and returns a list of matches.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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