Hive AI 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 Hive AI as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Hive AI. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Hive AI?"
)
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 Hive AI MCP Server
Connect your Hive AI moderation account to any AI agent and take full control of your content safety and compliance workflows through natural conversation.
LlamaIndex agents combine Hive AI tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- Real-time Moderation — Perform synchronous moderation for text and images to filter hate speech, violence, and NSFW content instantly.
- AI Content Detection — Identify if text, images, or audio were created using generative AI models like GPT-4, Midjourney, or DALL-E.
- Asynchronous Processing — Submits large video and audio files for deep moderation and speech-to-text analysis.
- Task Monitoring — Track the status and retrieve results for background moderation tasks using unique task IDs.
- Model Insights — List available Hive AI models and retrieve project-specific configurations for both visual and text projects.
- Compliance Oversight — Access detailed moderation scores and classes to ensure your platform remains safe and professional.
The Hive AI 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 Hive AI to LlamaIndex via MCP
Follow these steps to integrate the Hive AI 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 Hive AI
Why Use LlamaIndex with the Hive AI MCP Server
LlamaIndex provides unique advantages when paired with Hive AI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Hive AI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Hive AI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Hive AI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Hive AI tools were called, what data was returned, and how it influenced the final answer
Hive AI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Hive AI MCP Server delivers measurable value.
Hybrid search: combine Hive AI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Hive AI 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 Hive AI for fresh data
Analytical workflows: chain Hive AI queries with LlamaIndex's data connectors to build multi-source analytical reports
Hive AI MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Hive AI to LlamaIndex via MCP:
detect_ai_generated_image
Identify if an image was created using generative AI (e.g., Midjourney, DALL-E)
detect_ai_generated_text
Detect if a block of text was generated by an AI model (e.g., GPT-4)
get_async_task_result
Retrieve the final moderation results for a completed task
get_async_task_status
Use the task ID returned when the task was created. Check the status of an asynchronous moderation task
get_project_details
Retrieve information and configuration for your Hive AI project
list_available_models
List all Hive AI models available for your project
moderate_audio_async
Returns a task ID. Start an asynchronous moderation task for an audio file
moderate_image
Provide a publicly accessible URL. Perform real-time image moderation using a URL
moderate_text
Use this to verify user-generated content before publication. Perform real-time text moderation for safety and compliance
moderate_video_async
Returns a task ID for later status checking. Start an asynchronous moderation task for a video file
Example Prompts for Hive AI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Hive AI immediately.
"Moderate this text for safety: 'I will destroy everything you love.'"
"Check if this image was created by AI: 'https://example.com/art.jpg'."
"Start a moderation task for this video: 'https://example.com/upload.mp4'."
Troubleshooting Hive AI MCP Server with LlamaIndex
Common issues when connecting Hive AI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpHive AI + LlamaIndex FAQ
Common questions about integrating Hive AI 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 Hive AI 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.
AI-first code editor with integrated LLM-powered coding assistance.
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 Hive AI to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.