How to Use the Nylas MCP in LlamaIndex

Index your live inbox and calendar data into LlamaIndex for grounded, searchable AI answers.

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Connect Nylas MCP to LlamaIndex

Create your Vinkius account to connect Nylas 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 Nylas with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Semantic Inbox Search

Turn your email stream into a searchable knowledge base. By running `list_messages`, the agent pulls raw data that gets embedded directly into your vector store. This lets you query your past communications using natural language. The agent finds the context it needs without hallucinating details from thin air.

Grounded Scheduling

Stop guessing when you're free. The agent uses `list_events` to index your actual availability, ensuring every answer is based on your real-time calendar state. If you ask about your week, it cross-references your indexed data. It then uses `create_event` to fill gaps accurately.

Persistent Contact Knowledge

Keep your professional network within reach. `list_contacts` pulls your address book into the index, making your contacts part of the agent's long-term memory. Whenever you need a specific person's info, the agent retrieves it from the index. It updates the record via `create_contact` whenever new details arrive.

Setup guide

Set up Nylas 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 Nylas 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 Nylas tools.",
)
response = await agent.run("List recent Nylas data")

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Why Choose Vinkius

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Nylas MCP in LlamaIndex

Absolutely. The tool outputs are designed to be ingested into your vector index for retrieval-augmented generation.

It treats them as document chunks. Once retrieved, these chunks provide the grounding necessary for your agent to answer complex questions.

It can. The agent uses the same tools to write data back to your provider, ensuring your calendar stays in sync with your index.

No, the server is ephemeral. It fetches live data from the provider on demand, which your agent then indexes as needed.

Access is scoped to the specific tools provided. We never store your raw messages; the server simply acts as a conduit between your provider and your index.

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