OneNote MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add OneNote as an MCP tool provider through 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 OneNote. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in OneNote?"
)
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 OneNote MCP Server
Empower your conversational AI with omniscient access to your Microsoft OneNote digital brain. Transform how you organize information by linking an AI agent capable of drilling into deep notebooks, parsing hidden sections, explicitly searching across thousands of pages organically, and seamlessly capturing new notes from the chat interface without switching tabs.
LlamaIndex agents combine OneNote tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through 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
- Deep Search & Indexing — Invoke robust global searches discovering specific keywords across all active Notebook domains instantly without waiting for native indexing.
- Page Content Extraction — Drill cleanly into specific distinct Pages fetching raw text securely preventing you from hunting through nested hierarchies manually.
- Workspace Navigation — List structural trees mapping your distinct Notebooks, Section Groups, and Sections cleanly traversing your organizational logic entirely.
- Content Appending — Dispatch text blocks, parsed summaries, or quick thoughts into existing notebook sections cleanly right from the LLM prompt conversationally.
The OneNote MCP Server exposes 7 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 OneNote to LlamaIndex via MCP
Follow these steps to integrate the OneNote 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 7 tools from OneNote
Why Use LlamaIndex with the OneNote MCP Server
LlamaIndex provides unique advantages when paired with OneNote through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OneNote tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OneNote tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OneNote, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OneNote tools were called, what data was returned, and how it influenced the final answer
OneNote + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OneNote MCP Server delivers measurable value.
Hybrid search: combine OneNote real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OneNote 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 OneNote for fresh data
Analytical workflows: chain OneNote queries with LlamaIndex's data connectors to build multi-source analytical reports
OneNote MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect OneNote to LlamaIndex via MCP:
get_notebook
Use this to dive deeper into a container's permissions or basic configurations. Get detailed properties of a specific notebook
get_page_content
By default, OneNote pages are serialized using complex Microsoft Graph HTML formats with proprietary tags. Use this to ingest the actual written text or data. Retrieve the exact raw HTML content of a single page
list_notebooks
Identifies primary containers necessary to navigate the hierarchical structure of OneNote. List all Microsoft OneNote notebooks
list_pages
Results include the bare page metadata (IDs, titles, timestamps), but notably DO NOT include the heavy internal raw HTML content. Used for structural indexing. List all pages contained within a specific section
list_section_groups
Used for navigating highly complex, multi-layered textbook hierarchies inside OneNote. List section groups inside a specific notebook
list_sections
Sections act as the folders containing the raw pages. Requires passing the parent Notebook ID to query the correct topological children. List all sections contained within a specific notebook
search_pages
Useful when navigating deep, unindexed trees where discovering a particular keyword manually would exceed logic boundaries. Search page contents globally across all available notebooks
Example Prompts for OneNote in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OneNote immediately.
"Search globally across my entire OneNote environment finding paragraphs explicitly mentioning Project Alpha."
"List all active structural Notebooks visibly mapping currently in my domain."
"I need the exact written contents housed within page ID 1-3xx45... immediately."
Troubleshooting OneNote MCP Server with LlamaIndex
Common issues when connecting OneNote to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOneNote + LlamaIndex FAQ
Common questions about integrating OneNote 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 OneNote 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 OneNote to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.