Hiver MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create Shared Draft, Get Api Status, Get Conversation Details, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Hiver 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 App Connector for LlamaIndex
The Hiver app connector for LlamaIndex is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Hiver. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Hiver?"
)
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 Hiver MCP Server
Connect your Hiver account to any AI agent and transform your Gmail-based customer support into an intelligent, automated operation through natural conversation.
LlamaIndex agents combine Hiver 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
- Shared Inbox Management — List all shared mailboxes and retrieve detailed metadata for active email conversations
- Workflow Automation — Programmatically update conversation statuses (open, pending, closed) and manage assignments across your team
- Collaborative Drafting — Create shared drafts within Gmail threads directly through your agent to orchestrate perfect replies
- Tagging & Organization — Search and apply tags to categorize threads and maintain a high-fidelity collaboration ecosystem
- Team Visibility — List and search for inbox members to understand who is available and manage workload distribution
The Hiver 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.
All 12 Hiver tools available for LlamaIndex
When LlamaIndex connects to Hiver through Vinkius, your AI agent gets direct access to every tool listed below — spanning shared-inbox, gmail-integration, email-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Draft team reply
Check connection
Read email thread
Get mailbox info
List shared threads
List team members
Get mailbox tags
List Hiver inboxes
Find tags
Find members
Verify credentials
Modify conversation
Connect Hiver to LlamaIndex via MCP
Follow these steps to wire Hiver into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Hiver MCP Server
LlamaIndex provides unique advantages when paired with Hiver through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Hiver tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Hiver tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Hiver, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Hiver tools were called, what data was returned, and how it influenced the final answer
Hiver + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Hiver MCP Server delivers measurable value.
Hybrid search: combine Hiver real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Hiver 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 Hiver for fresh data
Analytical workflows: chain Hiver queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Hiver in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Hiver immediately.
"List all shared inboxes in my Hiver account."
"Show me the last 5 open conversations in the 'Support' inbox."
"Assign conversation 'thread-123' to user 'user-456' and add the tag 'priority'."
Troubleshooting Hiver MCP Server with LlamaIndex
Common issues when connecting Hiver to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHiver + LlamaIndex FAQ
Common questions about integrating Hiver MCP Server with LlamaIndex.