How to Use the PandaDoc MCP in LlamaIndex

Q: Can LlamaIndex search through PandaDoc templates?

Yes, use listtemplates to pull template data. You can index these structures to help your agent pick the right document format.

Q: Is PandaDoc document status queryable in LlamaIndex?

You can query the status of any document using getdocumentdetails. The agent reports the current state directly to you.

Index your PandaDoc contracts in LlamaIndex. Turn your document history into a searchable knowledge base.

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Connect PandaDoc MCP to LlamaIndex

Create your Vinkius account to connect PandaDoc 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 PandaDoc with LlamaIndex

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Index PandaDoc data with LlamaIndex

Run `list_documents` to fetch your contract history and ingest the results into your vector store. You can now perform semantic searches across your entire agreement archive. This turns static files into queryable knowledge. Your RAG app answers questions about past terms by looking at the actual document metadata.

Retrieve contract details via LlamaIndex

Use `get_document_details` to pull specific information into your index. When you need to verify if a contract was signed, the agent fetches the latest status. This ensures your knowledge base remains current. You get accurate answers grounded in the latest API state rather than stale data.

Automate document retrieval in LlamaIndex

The `get_download_link` tool lets your agent pull completed files for analysis. Once retrieved, the content is indexed to support deeper document understanding. Your agents can now compare terms across multiple signed agreements. It makes your document library a functional resource for automated review.

Setup guide

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

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