Evernote 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 Evernote 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 Evernote. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Evernote?"
)
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 Evernote MCP Server
Connect your Evernote account to any AI agent and take full control of your personal knowledge management and note-taking workflows through natural conversation.
LlamaIndex agents combine Evernote tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Note & Content Orchestration — Retrieve the full body and metadata of any note by GUID, including ENML body content and nested attachment attributes natively
- Semantic & Syntax Search — Execute powerful queries across all notebooks using Evernote's advanced syntax (keywords, tag filters, creation dates) to find information instantly
- Notebook Management — List all notebooks and retrieve detailed metadata including note counts and stack assignments to browse your workspace hierarchy
- Live Note Creation — Provision new notes inside specific notebooks by providing titles and plain-text or ENML content for immediate cross-device synchronization
- Categorical Tagging — Enumerate explicitly defined tags and manage nested tag hierarchies to filter and organize your personal database strictly
- Account & Quota Oversight — Fetch authenticated profile information including account tier, service level, and real-time quota usage to monitor system limits
- Metadata Auditing — Retrieve structural notebook representations and identify default status boundaries to manage your organizational topology flawlessly
The Evernote 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 Evernote to LlamaIndex via MCP
Follow these steps to integrate the Evernote 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 Evernote
Why Use LlamaIndex with the Evernote MCP Server
LlamaIndex provides unique advantages when paired with Evernote through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Evernote tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Evernote tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Evernote, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Evernote tools were called, what data was returned, and how it influenced the final answer
Evernote + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Evernote MCP Server delivers measurable value.
Hybrid search: combine Evernote real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Evernote 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 Evernote for fresh data
Analytical workflows: chain Evernote queries with LlamaIndex's data connectors to build multi-source analytical reports
Evernote MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Evernote to LlamaIndex via MCP:
create_note
The note is immediately synced and available across all Evernote clients. Create a new note inside a specified Evernote notebook
create_notebook
Returns the newly created notebook GUID and metadata. Create a new Evernote notebook
get_note
The content is returned in Evernote Markup Language (ENML). Retrieve the full content and metadata of a single Evernote note by GUID
get_notebook
Fetch detailed metadata for a specific Evernote notebook by its GUID
get_user
Get profile information for the currently authenticated Evernote user
list_notebooks
Use this to discover available notebooks before listing notes within them. Retrieve all Evernote notebooks for the authenticated account
list_notes
Use en.get_note to fetch full content. List up to 50 notes inside a specific Evernote notebook
list_tags
Useful for filtering and organizing notes. Retrieve all tags defined in the Evernote account
search_notes
Returns matching note metadata. Search across all Evernote notes using Evernote's powerful query syntax
update_note
This triggers a sync and increments the updateSequenceNum. Update the title and/or content of an existing Evernote note
Example Prompts for Evernote in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Evernote immediately.
"Create a note in 'Work' notebook with title 'Meeting Actions' and content 'Follow up with team.'"
"Search for notes containing 'recipe' and tagged 'favorite'"
"List all my notebooks and their note counts"
Troubleshooting Evernote MCP Server with LlamaIndex
Common issues when connecting Evernote to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEvernote + LlamaIndex FAQ
Common questions about integrating Evernote 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 Evernote 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.
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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 Evernote to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.