How to Use the DocsGenFlow MCP in LlamaIndex

Q: How do I connect DocsGenFlow to my LlamaIndex pipeline?

Run pip install llama-index-tools-mcp and instantiate BasicMCPClient. Wrap it in McpToolSpec and call totoollistasync to expose the document tools to your agent.

Q: Can LlamaIndex search through DocsGenFlow templates?

Yes, the agent uses listtemplates to fetch layout structures and indexes them. This lets your agent semantically match user requests to the correct document template.

Q: How do I download DocsGenFlow PDFs using LlamaIndex?

Your agent calls downloaddocument with the document ID retrieved from your query index. This returns the raw file data which you can write to local storage or send to the user.

Q: Does this MCP Server support resource loading in LlamaIndex?

Yes, you can set includeresources=True when configuring the client. This lets your indexer access template schemas and run logs directly as queryable data sources.

Index your generated PDF metadata directly into LlamaIndex vector stores for semantic document searches.

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Connect DocsGenFlow MCP to LlamaIndex

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Setup DocsGenFlow with LlamaIndex

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Index generated PDFs into your LlamaIndex RAG

`list_documents` pulls your complete generation history and feeds it directly into your MCP client's vector store. Your LlamaIndex agent converts this raw metadata into searchable nodes, letting you query past documents using natural language. This means you do not have to manually parse generated files. Your agent uses `get_document` to pull specific details and answer user queries based on active PDF data.

Ground agent answers in actual template schemas

`get_template` retrieves structural metadata so your LlamaIndex agent knows exactly which merge fields are available. You avoid hallucinations by forcing the agent to check the schema before running a document job. The agent queries `list_templates` to find matching layouts for a user's request. It matches the user's intent to an active layout, ensuring the `generate_document` call always has the correct variables.

Build a queryable archive of your document history

`list_history` exposes your generation logs to your indexer to build a timeline of user actions. Your agent searches this index to find when a specific contract or invoice was created. When a user asks to retrieve an old file, the agent calls `download_document` to fetch the binary payload. This turns your static document store into an interactive, conversational archive.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by DocsGenFlow. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about DocsGenFlow MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` and instantiate `BasicMCPClient`. Wrap it in `McpToolSpec` and call `to_tool_list_async` to expose the document tools to your agent.

Yes, the agent uses `list_templates` to fetch layout structures and indexes them. This lets your agent semantically match user requests to the correct document template.

Your agent calls `download_document` with the document ID retrieved from your query index. This returns the raw file data which you can write to local storage or send to the user.

Yes, you can set `include_resources=True` when configuring the client. This lets your indexer access template schemas and run logs directly as queryable data sources.

Your template HTML and merge JSON are processed in an isolated V8 sandbox on Vinkius. All communication is encrypted, and we handle API tokens securely so your credentials never leak.

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