How to Use the Bates Numbering Generator Engine MCP in LlamaIndex

Index your legal archives with LlamaIndex using the Bates Numbering Generator Engine.

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Connect Bates Numbering Generator Engine MCP to LlamaIndex

Create your Vinkius account to connect Bates Numbering Generator 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 Bates Numbering Generator Engine with LlamaIndex

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Automated indexing in LlamaIndex

The `generate_bates_numbers` tool creates the keys you need for your vector store. You generate the sequence once and use it to label your document nodes for retrieval. LlamaIndex treats these indices as metadata tags. This makes searching for a specific page in a massive discovery set fast and accurate.

Grounded document retrieval

Use this MCP server to ensure every document in your index has a valid identifier. You avoid the confusion of duplicate or missing numbers when querying your knowledge base. Your agents answer questions based on correctly numbered files. This grounding prevents your model from hallucinating file locations.

Unified API data access

Configure your tool spec to include resources for seamless integration. The server provides the numbering arrays, and your RAG pipeline handles the rest of the document ingestion. Building an index becomes a single workflow. You define the range, get the numbers, and attach them to your vectorized content.

Setup guide

Set up Bates Numbering Generator 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 Bates Numbering Generator 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 Bates Numbering Generator Engine tools.",
)
response = await agent.run("List recent Bates Numbering Generator Engine data")

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Common questions about Bates Numbering Generator Engine MCP in LlamaIndex

The framework ingests the generated arrays as metadata for your nodes. You can then filter or retrieve specific documents by their Bates identifier.

It is designed to feed into your ingestion pipeline. Generate the indices during the document load phase to ensure every file is tracked.

Simply run the tool again with your new parameters. The output is deterministic, so you always get the same numbering for the same inputs.

Yes, you define the prefix string in the tool arguments. The server appends your sequential integers to that prefix.

The server never receives your document content. It only handles the generation of identifiers, so your private legal files remain local to your own infrastructure.

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