How to Use the Gotenberg MCP in LlamaIndex

Q: Does the Gotenberg MCP Server support LlamaIndex tool filtering?

Yes, you can use the allowedtools filter to restrict which Gotenberg operations your LlamaIndex agent can perform. This adds control over your document generation process.

Index your live API data and documents into a searchable knowledge base using the Gotenberg MCP Server and LlamaIndex.

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Connect Gotenberg MCP to LlamaIndex

Create your Vinkius account to connect Gotenberg to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Gotenberg with LlamaIndex

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Turn API content into searchable RAG data

Convert web pages or raw HTML into PDFs with `convert_url_to_pdf` and automatically feed them into your LlamaIndex vector store. This creates a bridge between live web content and your semantic search index. Your application can then query these indexed documents alongside your other data sources. It keeps your knowledge base current without manual document uploads.

Gotenberg MCP Server for document indexing

Use `convert_markdown_to_pdf` to prepare your source files for ingestion into LlamaIndex. By standardizing your data as PDFs first, you ensure consistent parsing across your indexing pipeline. This approach allows you to maintain a unified format for all ingested documents. Your indexer handles the output while your agent manages the conversion parameters.

Automated PDF metadata indexing

Extract and index critical file information using `read_pdf_metadata` to improve your RAG retrieval accuracy. By adding metadata to your vectors, you provide the indexer with more context for every document. Your agents can then perform semantic searches that filter by these specific metadata fields. This results in more precise answers when querying your knowledge base.

Setup guide

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

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Common questions about Gotenberg MCP in LlamaIndex

Use the llama-index-tools-mcp package to instantiate the client. After that, convert the tools to a list and pass them to your FunctionAgent to start indexing.

Yes, you can use Gotenberg tools to generate PDFs from live sources. These documents are then indexed into your vector store for future retrieval.

Yes, you can use the allowed_tools filter to restrict which Gotenberg operations your LlamaIndex agent can perform. This adds control over your document generation process.

The generated PDF is passed directly to the tool spec. From there, your agent can index the content or store the file reference as part of your RAG workflow.

Your document contents are processed in memory and immediately discarded after the PDF is generated. The Gotenberg MCP Server acts as a stateless conduit between your data and your index.

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