Amplenote MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Amplenote as an MCP tool provider through 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 Amplenote. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Amplenote?"
)
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 Amplenote MCP Server
Connect your Amplenote account to any AI agent to fuse your personal knowledge base and task manager directly into your daily computational workflows.
LlamaIndex agents combine Amplenote tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Notes & Ideas — Read, create, list, and natively search your entire note library to pull exact context into your AI conversations seamlessly.
- Task Execution — Query specific pending to-dos, update task states, or rapidly create new tasks within specific notes without leaving the chat.
- Tag Management — Dynamically list and analyze the tag hierarchy of your Amplenote system, keeping the AI aware of your organizational framework.
- Action Tracking — Instruct the agent to invoke native Amplenote actions, maintaining deep synchronization between the AI and your existing mental models.
The Amplenote MCP Server exposes 12 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 Amplenote to LlamaIndex via MCP
Follow these steps to integrate the Amplenote 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 12 tools from Amplenote
Why Use LlamaIndex with the Amplenote MCP Server
LlamaIndex provides unique advantages when paired with Amplenote through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Amplenote tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Amplenote tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Amplenote, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Amplenote tools were called, what data was returned, and how it influenced the final answer
Amplenote + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Amplenote MCP Server delivers measurable value.
Hybrid search: combine Amplenote real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Amplenote 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 Amplenote for fresh data
Analytical workflows: chain Amplenote queries with LlamaIndex's data connectors to build multi-source analytical reports
Amplenote MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Amplenote to LlamaIndex via MCP:
create_note
Use for adding documentation, meeting notes, or project plans. Create a new note with a title and Markdown body content
create_task
Tasks in Amplenote live inside notes and have due dates, priorities, and completion tracking. Use for adding actionable items. Create a new task
delete_note
Permanently delete a note by UUID
get_note
Essential for reading or analyzing a specific document. Retrieve the full content and metadata of a specific note by UUID
get_note_actions
Use to discover what operations can be performed on a note. Retrieve available actions for a specific note
get_task
Use to inspect or update a single task. Retrieve a specific task by its ID
list_notes
Use as the primary way to browse the entire knowledge base. List all notes in the Amplenote workspace
list_tags
Returns tag names and usage counts. Use to discover the knowledge taxonomy. List all tags used across notes and tasks
list_tasks
Returns task content, completion status, due dates, and parent note references. Use for task management overview. List all tasks across all notes
search_notes
Use when the user wants to find content by keyword. Full-text search across all Amplenote notes and tasks
update_note
Use for editing content, fixing errors, or appending information. Update an existing note title and/or Markdown body by UUID
update_task
Use for task progress tracking and management. Update a task content, completion status, or other properties
Example Prompts for Amplenote in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Amplenote immediately.
"Create a new note titled 'Project Alpha Planning' and assign it the tag '#work/projects'."
"Search my Amplenote vault for all active tasks containing the word 'Budget'."
"Get the content of my 'Weekly Sync' note."
Troubleshooting Amplenote MCP Server with LlamaIndex
Common issues when connecting Amplenote to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpAmplenote + LlamaIndex FAQ
Common questions about integrating Amplenote 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 Amplenote 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 Amplenote to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.