How to Use the Evernote MCP in LlamaIndex

Q: Can LlamaIndex update an existing Evernote document?

Yes. The agent uses updatenote to modify the title or content of a specific note GUID.

Q: How do I isolate specific Evernote notebooks in LlamaIndex?

Run listnotebooks to get the GUID. Then pass that ID to listnotes to restrict your data ingestion to that folder.

Turn your Evernote notebooks into a searchable LlamaIndex knowledge base using this MCP server.

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Connect Evernote MCP to LlamaIndex

Create your Vinkius account to connect Evernote to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Evernote with LlamaIndex

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Index raw ENML content

The `get_note` tool feeds raw ENML content directly into your LlamaIndex vector store. Your application ingests entire notebooks and converts them into queryable embeddings. Instead of relying on basic keyword matching, you build RAG pipelines over your personal archives. The agent pulls the text, indexes it, and grounds its answers in your actual notes rather than hallucinating.

Batch process specific directories

The `list_notes` tool pulls batches of 50 notes at a time from a specific notebook. You use this to run bulk indexing jobs across targeted sections of your account. Before pulling the notes, your setup calls `list_notebooks` to map out the directory structure. This lets you isolate personal drafts from work documents when building your index.

Pre-filter data with the MCP Server

The `search_notes` tool acts as a pre-filter for the MCP Server before indexing begins. You pass query syntax to grab specific subsets of data. If your agent generates new insights from the index, it can write them back using `create_note`. This creates a loop where your RAG application reads from and writes to the same ecosystem.

Setup guide

Set up Evernote MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Evernote MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Evernote tools.",
)
response = await agent.run("List recent Evernote data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Evernote. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Evernote MCP in LlamaIndex

Install llama-index-tools-mcp. Initialize a BasicMCPClient, wrap it in McpToolSpec, and call to_tool_list_async().

Yes. The agent uses update_note to modify the title or content of a specific note GUID.

The API returns ENML format. Your text splitters will need to parse the XML tags before generating embeddings.

Run list_notebooks to get the GUID. Then pass that ID to list_notes to restrict your data ingestion to that folder.

The server processes your profile details and personal notes. The zero-trust architecture means traffic routes through a single-use endpoint token, and no data persists after the session closes.

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