ProcessOn 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 ProcessOn as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 ProcessOn. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in ProcessOn?"
)
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 ProcessOn MCP Server
Empower your AI agent to orchestrate your visual documentation with ProcessOn, the premier online platform for flowcharts, mind maps, and organizational charts. By connecting ProcessOn to your agent, you transform complex diagram management and project coordination into a natural conversation. Your agent can instantly list your files, create new diagrams, export your work into multiple formats, and even monitor collaborators without you ever needing to navigate the web interface. Whether you are designing a system architecture or a complex business process, your agent acts as a real-time visual documentation assistant, keeping your diagrams organized and your production moving.
LlamaIndex agents combine ProcessOn 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
- Diagram Orchestration — List all accessible flowcharts, mind maps, and diagrams across your ProcessOn workspace.
- File Management — Create, retrieve, and delete diagrams with full support for collaborative metadata.
- Export Control — Seamlessly export diagrams into standard formats like png, pdf, svg, and Visio.
- Folder Organization — Browse folder structures and manage diagram locations efficiently.
- Collaboration Monitoring — List file collaborators and manage access insights for your team.
The ProcessOn 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 ProcessOn to LlamaIndex via MCP
Follow these steps to integrate the ProcessOn 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 ProcessOn
Why Use LlamaIndex with the ProcessOn MCP Server
LlamaIndex provides unique advantages when paired with ProcessOn through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine ProcessOn tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain ProcessOn tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query ProcessOn, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what ProcessOn tools were called, what data was returned, and how it influenced the final answer
ProcessOn + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the ProcessOn MCP Server delivers measurable value.
Hybrid search: combine ProcessOn real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query ProcessOn 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 ProcessOn for fresh data
Analytical workflows: chain ProcessOn queries with LlamaIndex's data connectors to build multi-source analytical reports
ProcessOn MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect ProcessOn to LlamaIndex via MCP:
create_file
Create a new diagram
delete_file
Delete a diagram
export_file
Export a diagram
get_file
Get diagram file details
get_folder_content
Get folder contents
get_org_info
Get organization details
get_recent_files
Get recent files
list_collaborators
List file collaborators
list_files
List all ProcessOn files
list_folders
List all folders
Example Prompts for ProcessOn in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with ProcessOn immediately.
"List all my flowcharts on ProcessOn."
"Create a new mind map titled 'Q4 Goals' in the 'Planning' folder."
"Export the diagram 'Architecture V2' to PNG format."
Troubleshooting ProcessOn MCP Server with LlamaIndex
Common issues when connecting ProcessOn to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpProcessOn + LlamaIndex FAQ
Common questions about integrating ProcessOn 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?
Connect ProcessOn with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect ProcessOn to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.