How do I find my Missive Access Token?

Go to Missive Settings > API and generate a new Bearer token.

Do I need a specific Missive plan to use the API?

Yes, API access typically requires a Productive or Business plan.

Can I perform actions like closing a conversation?

Yes, use the 'create_post' tool and set the 'close' parameter to true.

Missive MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Missive
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Missive as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Missive. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Missive?"
    )
    print(response)

asyncio.run(main())

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About Missive MCP Server

Connect Missive to your AI agent and manage your team's communication efficiently. Access conversations, messages, and contacts through natural conversation to stay organized and responsive.

LlamaIndex agents combine Missive 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

Conversation Management — List and view conversations from any mailbox (inbox, assigned, closed).
Collaboration — Post internal comments or trigger actions like closing and assigning conversations.
Message Access — Read all messages and comments within a specific conversation thread.
Contact Organization — Search for and create contacts in your shared or private contact books.
Draft & Send — Create email drafts and deliver them directly from your AI agent.

The Missive 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 Missive to LlamaIndex via MCP

Follow these steps to integrate the Missive 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 Missive

Why Use LlamaIndex with the Missive MCP Server

LlamaIndex provides unique advantages when paired with Missive through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Missive tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Missive tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Missive, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Missive tools were called, what data was returned, and how it influenced the final answer

Missive + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Missive MCP Server delivers measurable value.

Hybrid search: combine Missive real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Missive 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 Missive for fresh data

Analytical workflows: chain Missive queries with LlamaIndex's data connectors to build multi-source analytical reports

Missive MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Missive to LlamaIndex via MCP:

create_contact

Create a new contact

create_draft

Create an email draft

create_post

Can also trigger actions like closing, assigning, or labeling. Create a post (comment or action) in a conversation

get_conversation

Get details for a specific conversation

get_me

Get current Missive user details

list_contacts

List Missive contacts

list_conversations

A mailbox filter is required (e.g., "inbox", "all", "assigned", "closed"). List conversations from a specific mailbox

list_labels

List Missive labels

list_messages

List messages in a conversation

send_draft

Send a prepared draft

Example Prompts for Missive in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Missive immediately.

"List my recent conversations in the inbox."

"Add a comment 'Working on this now' to conversation id 123."

"Find contact info for 'Jane Smith'."

Troubleshooting Missive MCP Server with LlamaIndex

Common issues when connecting Missive to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Missive + LlamaIndex FAQ

Common questions about integrating Missive MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Missive tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.