PandaDoc MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PandaDoc as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to PandaDoc. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PandaDoc?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About PandaDoc MCP Server
Connect your PandaDoc account to any AI agent and automate your document workflows through natural conversation.
LlamaIndex agents combine PandaDoc tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Documents — List, create from templates, send for signature, check status, and track viewed/completed/declined documents
- Templates — Browse all available document templates (proposals, contracts, NDAs, quotes)
- E-Signatures — Send documents for signature and monitor signing progress in real time
- Contacts — Manage recipient contacts with email, name, and company
- Team — List workspace members and their roles
The PandaDoc MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect PandaDoc to LlamaIndex via MCP
Follow these steps to integrate the PandaDoc MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from PandaDoc
Why Use LlamaIndex with the PandaDoc MCP Server
LlamaIndex provides unique advantages when paired with PandaDoc through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PandaDoc tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PandaDoc tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PandaDoc, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PandaDoc tools were called, what data was returned, and how it influenced the final answer
PandaDoc + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PandaDoc MCP Server delivers measurable value.
Hybrid search: combine PandaDoc real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PandaDoc to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying PandaDoc for fresh data
Analytical workflows: chain PandaDoc queries with LlamaIndex's data connectors to build multi-source analytical reports
PandaDoc MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PandaDoc to LlamaIndex via MCP:
pandadoc_create_contact
Email is required. Once created, patients can be used as recipients in document creation. Returns the created contact with their PandaDoc ID. Create a new contact in PandaDoc with email, name, and company for use as a document recipient
pandadoc_create_document
templateId is required (use pandadoc_list_templates to find). Recipients array must include at least email and optionally first_name, last_name, and role (matching template roles). The document is created in "uploaded" status and transitions to "draft" within 3-5 seconds. Fields is an optional JSON object to pre-fill template tokens/variables. Create a new PandaDoc document from a template with recipients, custom fields, and pricing — ready to send for signature
pandadoc_delete_document
This is irreversible. Only documents in draft or voided status should typically be deleted. Completed/signed documents should be voided first if deletion is required for compliance reasons. Permanently delete a PandaDoc document — this action cannot be undone and removes the document from all views
pandadoc_document_status
Returns current status, last viewed/completed dates, and recipient progress. Use for tracking: "has the client signed?", "did they view it?", or status polling after sending. Check the current status of a PandaDoc document — whether it is draft, sent, viewed, completed, or declined
pandadoc_get_document
Returns document name, status, all recipients with their signing status, template reference, pricing table totals, custom field values, and metadata. Use after listing documents to drill into a specific document for complete information. Get complete details of a specific PandaDoc document by ID, including recipients, fields, tokens, pricing, and audit trail
pandadoc_list_contacts
Returns contact name, email, company, and metadata. Contacts are the people your organization sends documents to. Use when the user asks about recipients, needs to find a contact email, or wants to review the contact database. List PandaDoc contacts with names, emails, companies, and associated document history
pandadoc_list_documents
Filter by status: draft (not yet sent), sent (awaiting signatures), completed (fully signed), viewed (opened by recipient), paid, voided, or declined. Returns document name, template used, status, total value, owner email, and dates. Use when the user asks about document pipeline, pending signatures, or completed agreements. List PandaDoc documents with name, status (draft/sent/completed/viewed/paid/voided/declined), creation date, and recipient info
pandadoc_list_members
Returns member name, email, role, and status. Use when the user asks about team members, document ownership, or needs to audit workspace access. List workspace members (users) in your PandaDoc organization with their email, role, and access level
pandadoc_list_templates
Returns template name, UUID (needed for pandadoc_create_document), creation date, and folder. Templates are reusable document blueprints with pre-defined layouts, fields, and recipient roles. Use when the user asks "what templates do we have?" or needs a template ID before creating a document. List all PandaDoc templates available for document creation — proposals, contracts, agreements, NDAs, and more
pandadoc_send_document
This triggers email notifications to all recipients. Set silent=true to suppress emails (useful when embedding signing in your own app). An optional message can be included in the notification email. The document moves to "sent" status after this call. Send a PandaDoc document for signature — transitions it from draft to sent and notifies all recipients via email
Example Prompts for PandaDoc in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PandaDoc immediately.
"Show me all proposals waiting for signature"
"Create a new NDA for Jane Doe at Global Solutions."
"Did Acme Corp sign the contract I sent yesterday?"
Troubleshooting PandaDoc MCP Server with LlamaIndex
Common issues when connecting PandaDoc to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPandaDoc + LlamaIndex FAQ
Common questions about integrating PandaDoc MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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Connect PandaDoc to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.