How to Use the Brazilian Judiciary Dictionary Engine MCP in LlamaIndex

Index Brazilian legal entities into your LlamaIndex knowledge base using the Brazilian Judiciary Dictionary Engine.

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Connect Brazilian Judiciary Dictionary Engine MCP to LlamaIndex

Create your Vinkius account to connect Brazilian Judiciary Dictionary Engine to LlamaIndex 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 LlamaIndex

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Index court mentions in LlamaIndex

Use `search_legal_entities` to pull out tribunal names from your PDFs. LlamaIndex then stores these as searchable nodes in your vector index. You gain a knowledge base where every court reference is a queryable data point. Stop searching raw text and start querying your index for specific agencies.

Ground LlamaIndex answers with dictionary data

The agent pulls live legal data to verify document claims. It calls the server, gets the entity list, and anchors your RAG responses in facts. This stops your agent from hallucinating court names. It only cites entities verified by the dictionary.

Filter tools for targeted LlamaIndex retrieval

Restrict the agent to using only the dictionary tool when parsing new legal filings. This keeps your index clean and focused on entity extraction. Toggle the allowed_tools filter to ensure the agent doesn't waste tokens on irrelevant lookups. It stays on task for every document.

Setup guide

Set up Brazilian Judiciary Dictionary Engine MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Brazilian Judiciary Dictionary Engine MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Brazilian Judiciary Dictionary Engine tools.",
)
response = await agent.run("List recent Brazilian Judiciary Dictionary Engine data")

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Common questions about Brazilian Judiciary Dictionary Engine MCP in LlamaIndex

It is. You wrap the server in a tool spec and pass the results to your agent. From there, you can index those findings into your vector store.

The output is returned as structured text. You can then parse this into your knowledge base or use it to filter search results.

You can. Run the tool over new documents to identify entities, then append those findings to your existing index.

It uses standard HTTP transport with your endpoint token. Your legal data never hits external training sets.

No. The server only scans the text you pass in the request. It holds no persistent records of your document contents.

Use it with your favorite AI tools

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