How to Use the ThePDFMaker MCP in LlamaIndex

Q: How does ThePDFMaker integrate with LlamaIndex for invoice generation?

You use the RAG pattern: first, you run generatepdf to get a document. Then, the resulting metadata (template ID, creation date) is indexed into your vector store. Your agent can then query this knowledge base about that specific invoice.

Q: Can LlamaIndex help me find template information for ThePDFMaker?

Yes. You can index the output of gettemplatedetails and listpdftemplates. Instead of just listing them, your agent can answer complex questions like, 'Which templates are suitable for quarterly financial reports?'

Q: What if I need to delete a template using ThePDFMaker with LlamaIndex?

You run deletepdftemplate, and the confirmation details of that action are indexed. This creates a searchable record, allowing future queries to confirm who deleted what and when.

Q: Does ThePDFMaker support retrieving user profiles for LlamaIndex?

Yes. You can index the data returned by getuserprofile. This allows your RAG application to answer questions about account ownership or specific user permissions based on the API responses.

Q: How do I check ThePDFMaker's status and usage in LlamaIndex?

You can index the results of both getapistatus and getcreditbalance. This lets your knowledge base answer queries like, 'Is the PDF generation API currently online?' or 'What is our current credit limit?'

Build RAG systems with LlamaIndex. Index ThePDFMaker results into a knowledge base for semantic search.

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Setup ThePDFMaker with LlamaIndex

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Indexing Document Metadata

Every API call result can become searchable data. When you use `generate_pdf`, the output status and job ID are automatically indexed alongside the template details. You'll get answers grounded in actual PDF generation attempts. Need to know which templates exist? Indexing results from `list_pdf_templates` allows your agent to answer questions like, 'What reports can we make?' using semantic search.

Tracking Usage and Credits

Don't let the knowledge base get fuzzy on costs. Indexing output from `get_credit_balance` lets your RAG application answer questions about spending limits. Similarly, querying past usage via `list_recent_pdfs` means users can ask, 'How many invoices did we generate last month?' and get a precise answer.

Managing Webhook Definitions

The configuration details for webhooks are valuable knowledge. Indexing the output of `list_webhooks` means your system can semantically search through webhook definitions. If you need to know how to clean up old jobs, indexing `delete_pdf_template` results helps document the deletion process and its impact.

Setup guide

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

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Common questions about ThePDFMaker MCP in LlamaIndex

You use the RAG pattern: first, you run `generate_pdf` to get a document. Then, the resulting metadata (template ID, creation date) is indexed into your vector store. Your agent can then query this knowledge base about that specific invoice.

Yes. You can index the output of `get_template_details` and `list_pdf_templates`. Instead of just listing them, your agent can answer complex questions like, 'Which templates are suitable for quarterly financial reports?'

You run `delete_pdf_template`, and the confirmation details of that action are indexed. This creates a searchable record, allowing future queries to confirm who deleted what and when.

Yes. You can index the data returned by `get_user_profile`. This allows your RAG application to answer questions about account ownership or specific user permissions based on the API responses.

You can index the results of both `get_api_status` and `get_credit_balance`. This lets your knowledge base answer queries like, 'Is the PDF generation API currently online?' or 'What is our current credit limit?'

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