How to Use the CNJ (Datajud API Pública) MCP in LangChain

Q: How do I search for a case number using CNJ (Datajud API Pública) in LangChain?

You pass your case string to the searchprocessbynumber tool within your agent definition. The MCP server handles the connection and returns the record directly to your LangChain pipeline.

Build complex reasoning chains with CNJ (Datajud API Pública) data inside LangChain for automated legal research pipelines.

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Connect CNJ (Datajud API Pública) MCP to LangChain

Create your Vinkius account to connect CNJ (Datajud API Pública) 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 CNJ (Datajud API Pública) with LangChain

Ask AI about this MCP

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Claude

Perplexity

Chain judicial data into LangGraph agents

Feed specific case numbers into `search_process_by_number` to kick off a multi-step investigation. Your agent pulls the raw status and uses that output to trigger subsequent analysis tasks without human intervention. LangChain tracks these transitions through LangSmith. You see exactly how the agent moves from an initial query to a final conclusion using the data it retrieved.

Run Elasticsearch queries through LangChain

Execute complex searches using `search_processes_advanced` directly within your agent logic. This gives your pipeline direct access to the Datajud index for finding patterns in massive case volumes. You avoid manual filtering because the agent parses the API results into the next step of your chain. It turns raw judicial records into structured insights immediately.

Filter cases by court and class

Pass specific procedural classes to `search_processes_by_class_and_organ` to narrow down your research focus. This tool lets your chain ignore irrelevant records and target exactly what you need. Your agent builds a context-aware workflow by grouping these specific results. It creates a clean chain of information that stays focused on the relevant jurisdiction.

Setup guide

Set up CNJ (Datajud API Pública) 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 CNJ (Datajud API Pública) 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({
    "cnj-datajud-api-publica-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 CNJ (Datajud API Pública) 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 CNJ Datajud. 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 CNJ (Datajud API Pública) MCP in LangChain

You pass your case string to the `search_process_by_number` tool within your agent definition. The MCP server handles the connection and returns the record directly to your LangChain pipeline.

Yes, you connect the tool to your agent and define a chain that iterates through search results. The agent takes the output from the API and summarizes it for your reports.

It acts as just another tool in your array. You combine it with your existing vector stores and databases to build a unified research system.

Vinkius handles the transport, but the server itself is stateless. Your queries are ephemeral and forgotten as soon as the session ends.

The server only touches public judicial process records and procedural metadata. We enforce a strict zero-trust boundary so your proprietary search logic stays on your machine.

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