How to Use the Alegra MCP in LlamaIndex

Q: How can I use LlamaIndex and Alegra to analyze sales?

Your LlamaIndex agent can periodically call listinvoices to fetch sales data. It then indexes this data, letting you ask natural language questions about sales trends, top customers, and revenue over time.

Q: Is it hard to set up Alegra with a LlamaIndex agent?

It's straightforward. You'll use the McpToolSpec from the llama-index-tools-mcp package. Just point it to your Vinkius URL, and it generates the tools for your agent to use.

Q: Can my agent create new invoices in Alegra from LlamaIndex?

Absolutely. The tool spec includes createinvoice and createcontact. Your RAG application can extract details from a document and then use these tools to generate the corresponding records in Alegra.

Turn your Alegra business data into a queryable knowledge base. Let your LlamaIndex RAG app answer questions about invoices and inventory.

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Connect Alegra MCP to LlamaIndex

Create your Vinkius account to connect Alegra 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 Alegra with LlamaIndex

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Perplexity

Ask Questions About Your Business

Connect Alegra to LlamaIndex and stop digging for data. Instead of manually running reports, just ask your app: "Which clients have unpaid invoices from last month?" The agent will use `list_invoices` and `get_contact_details` to find the answer. The real power is that LlamaIndex remembers the results. It indexes the output from the MCP tools, creating a knowledge base from your live business data. Your next query will be faster and grounded in facts from previous API calls.

Build a RAG Pipeline on Live Inventory Data

Your agent can build a searchable index of your entire product catalog. It uses `list_inventory_items` and `get_item_details` to fetch all your stock data from Alegra. Now you can perform semantic searches on your inventory. Ask things like "Do we have any products similar to 'Product X' in stock?" Your LlamaIndex application will query the vector store it built from your Alegra data, giving you answers that a simple database query couldn't.

Ground Your LlamaIndex Agent in Financial Facts

Stop agent hallucinations when it comes to your company's finances. By using this MCP server, your LlamaIndex agent's responses are based on actual data from `list_invoices`, `list_payments`, and `list_estimates`. When you ask, "What were our total payments in Q3?", the agent calls the `list_payments` tool, indexes the results, and synthesizes an answer. The response includes citations back to the raw data, so you can always check its work.

Setup guide

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

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Common questions about Alegra MCP in LlamaIndex

Your LlamaIndex agent can periodically call `list_invoices` to fetch sales data. It then indexes this data, letting you ask natural language questions about sales trends, top customers, and revenue over time.

Yes, that's what LlamaIndex is built for. You can index your Alegra invoice data alongside PDFs of your contracts or text from your emails. This creates a unified knowledge base across all your business information.

It's straightforward. You'll use the `McpToolSpec` from the `llama-index-tools-mcp` package. Just point it to your Vinkius URL, and it generates the tools for your agent to use.

Absolutely. The tool spec includes `create_invoice` and `create_contact`. Your RAG application can extract details from a document and then use these tools to generate the corresponding records in Alegra.

LlamaIndex agents access your Alegra contact and invoice information via this MCP server. The data is fetched on-demand for indexing. The Vinkius platform ensures each request is isolated and authenticated, so your financial data is only exposed to your configured agent.

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