How to Use the Alphamoon MCP in LlamaIndex

Index your document data into LlamaIndex using the Alphamoon MCP Server for grounded, search-backed document intelligence.

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Connect Alphamoon MCP to LlamaIndex

Create your Vinkius account to connect Alphamoon 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 Alphamoon with LlamaIndex

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Index Alphamoon results in LlamaIndex

Convert document extraction output into searchable knowledge. After calling `get_ocr_results`, you can feed that text into your vector store to query your document history. This approach grounds your answers in real API data. Instead of relying on model memory, the agent references the indexed results from your processed contracts.

Query document history via LlamaIndex

Build RAG applications that understand your document library. Use `list_documents` to find the right files, then pull the content for the index. Your agent can now answer questions about specific invoice details. It looks up the document, parses the extraction, and provides a cited response.

Filter document access for LlamaIndex

Restrict which tools your agent can access. By using the allowed_tools filter, you ensure the agent only interacts with specific functions like `get_document`. This protects your document repository from accidental changes. You maintain full control over which parts of your data are searchable.

Setup guide

Set up Alphamoon 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 Alphamoon 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 Alphamoon tools.",
)
response = await agent.run("List recent Alphamoon data")

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Common questions about Alphamoon MCP in LlamaIndex

You convert the tool output into a list and add it to your knowledge base. This makes document content searchable by your RAG agent.

Absolutely, you map the tools to your agent. The agent then calls them to gather data before generating an answer.

Yes, once you index the extraction results, they become part of your queryable knowledge base. You can ask questions about your documents.

It handles contracts, invoices, and forms. The server extracts text and data, which you then index for your search tasks.

Your documents remain in the server's sandbox. Only the extracted text you explicitly index is stored in your local vector store.

Use it with your favorite AI tools

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

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Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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