Hive AI MCP. Stop reviewing content manually. Automate safety checks now.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Hive AI is an MCP Server that automates content safety checks across text, images, audio, and video files. Your agent can perform real-time moderation to filter out hate speech or NSFW material.
It also detects if media was generated by tools like Midjourney or GPT-4. Use it when you need built-in compliance oversight for user-generated content.
What your AI agents can do
Detect ai generated image
Checks if an uploaded image was created by generative AI (like Midjourney or DALL-E).
Detect ai generated text
Detects if a block of text passed into the tool came from an artificial intelligence model.
Get async task result
Retrieves the final moderation report for a background task using its unique ID.
Run real-time moderation on user text to flag hate speech, profanity, or policy violations.
Analyze images via a URL for NSFW content and general safety compliance in real time.
Check text or images to see the probability that they were generated by large language models or diffusion models.
Submit a video file for deep, asynchronous analysis and receive a unique task ID for later retrieval.
Start an async job to moderate audio files, including transcription and safety checks.
Use a task ID to check the status or retrieve final results for long-running moderation jobs.
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
Hive AI MCP Server: 10 Tools for Media Safety
Use these tools to perform comprehensive moderation across images, text, audio, and video. Check for safety violations or detect artificial content origins.
019d75b1detect ai generated image
Checks if an uploaded image was created by generative AI (like Midjourney or DALL-E).
019d75b1detect ai generated text
Detects if a block of text passed into the tool came from an artificial intelligence model.
019d75b1get async task result
Retrieves the final moderation report for a background task using its unique ID.
019d75b1get async task status
Checks if an asynchronous moderation job is finished or still processing, requiring a task ID.
019d75b1get project details
Retrieves configuration and metadata for your specific Hive AI project setup.
019d75b1list available models
Lists all the moderation models available in your account, helping you choose the right one.
019d75b1moderate audio async
Starts a background job to moderate an audio file and returns a task ID for later status checks.
019d75b1moderate image
Performs real-time safety moderation on an image provided via a public URL.
019d75b1moderate text
Runs immediate, real-time checks on text to verify if user-generated content is safe for publication.
019d75b1moderate video async
Starts a background moderation task for a video file and returns a unique ID.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Hive AI, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Your agent runs content safety checks across text, images, audio, and video. You don't gotta manually review every post; your MCP Server handles the heavy lifting.
Checking Text Safety
For immediate oversight, you can run real-time moderation on any block of user-generated text using moderate_text. This instantly flags things like hate speech, profanity, or general policy violations so you know if content is safe for publication right away.
Moderating Images
Need to check an image? You pass it via a public URL and use moderate_image for real-time safety checks. It analyzes the picture immediately for NSFW material and general compliance issues, giving you instant feedback on whether the visual content is clean.
Detecting AI Content
To catch deepfakes or machine garbage, your agent has two specific tools. You use detect_ai_generated_text to check a text block and see if it came from some large language model. Similarly, you run detect_ai_generated_image when you want to know if an uploaded image was created by generative AI like Midjourney or DALL-E.
Handling Large Files (Asynchronous Jobs)
When content is too big for a quick check—think long videos or complex audio files—you gotta use background jobs. You kick off video moderation with moderate_video_async, which returns a unique ID. If you're working with sound, you start the process using moderate_audio_async; this job handles both safety checks and transcription for your audio file, and it hands back another unique task ID.
Since these jobs take time, you can't just wait around. You use the returned task IDs to keep track. To see if a background moderation job is done or still running, you call get_async_task_status. Once that status check says everything’s good, you grab the final results by calling get_async_task_result, which pulls the full moderation report for you.
Setup and Utility Tools
Before you start moderating, you might need setup info. You can use list_available_models to see every single moderation model available in your account so you know which one to pick. If you're not sure about the project configuration, running get_project_details retrieves all the metadata for your specific Hive AI setup.
This gives you a clear picture of how the system is configured before you start processing content.
How Hive AI MCP Works
- 1 Subscribe to the server and input your Hive AI Visual and Text project API Keys.
- 2 Your agent calls a specific tool (e.g.,
moderate_text) with the content URL or text block. - 3 The system returns an immediate safety score, flags violations, or, for deep analysis, provides a task ID to check later.
The bottom line is: you pass bad content through your agent's moderation tools before it hits production.
Who Is Hive AI MCP For?
This server is for anyone whose job involves reviewing user-generated data. Think Trust & Safety teams who need to automate filtering rules, community managers monitoring chat uploads, or developers integrating compliance checks directly into a data pipeline.
Uses get_async_task_status and moderation scores to automatically filter content that violates platform rules.
Runs real-time checks on image uploads using moderate_image to maintain community standards instantly.
Integrates deep content analysis and AI detection into the app's data flow, using tools like detect_ai_generated_text before saving records.
What Changes When You Connect
- Real-Time Filtering: Use
moderate_textto vet posts before they go live. Your agent instantly scores the content for hate speech or profanity, blocking bad inputs immediately. - AI Origin Tracking: The
detect_ai_generated_imageanddetect_ai_generated_texttools let you audit whether submitted media was actually human-created, which is critical for compliance. - Deep Media Analysis: Don't worry about large files. Send video or audio via
moderate_video_asyncormoderate_audio_async, get a task ID, and useget_async_task_resultlater when the report is ready. - System Oversight: Use
list_available_modelsto understand what's available in your account. You can also check project configuration withget_project_details. - Workflow Control: The combination of status checking (
get_async_task_status) and result retrieval gives you full control over complex, multi-step moderation workflows.
Real-World Use Cases
Moderating a large user forum dump
A platform developer receives 5,000 new posts. Instead of running manual checks, the agent loops through them, calling moderate_text on each one. The client filters out any content with a high 'Violence' score before committing the data to the database.
Reviewing an uploaded video submission
A user uploads a 10-minute promotional video. The agent calls moderate_video_async, which returns task ID 'X'. Minutes later, the system polls this ID using get_async_task_status until it retrieves the full safety report via get_async_task_result.
Checking image provenance on an art site
A community manager wants to verify if user-submitted artwork is AI-generated. The agent takes the image URL and runs it through detect_ai_generated_image. If the probability exceeds 95%, the submission is flagged for manual review.
Maintaining chat room standards
In a live chat environment, users post images. The agent intercepts the URL and calls moderate_image in real time. If the image fails the safety check, the agent immediately blocks the message before anyone sees it.
The Tradeoffs
Assuming synchronous results
A developer tries to get a result for a 10-minute video by calling get_async_task_result immediately after starting the job. It fails because the task isn't done.
→
Always start with moderate_video_async, capture the resulting task ID, and then use get_async_task_status repeatedly until it confirms 'completed'. Only then call get_async_task_result.
Mixing text and image checks
The agent tries to send both a block of text and an image URL in one single request, assuming the tool handles both types simultaneously.
→
You must use separate tools. Call moderate_text for the writing, and then call moderate_image with the URL for the picture. Run them sequentially.
Ignoring model limits
The developer assumes all moderation is free or uses a single default setting without checking what's available.
→
Run list_available_models first. This shows you exactly which models are configured for your project and helps prevent using the wrong service.
When It Fits, When It Doesn't
Use this server if compliance, safety, or provenance is a primary concern for user-generated content. If your application needs to filter text before publishing (use moderate_text), validate images/video uploads (moderate_image/detect_ai_generated_image), or analyze large files asynchronously (moderate_audio_async/moderate_video_async), this is the right pick.
Don't use it if you only need to perform simple data transformations (like JSON parsing) or basic message routing. For those, a general-purpose messaging tool will suffice. Also, don't try to moderate content that hasn't been uploaded yet—you must have a URL or text string to pass into the tools.
This server requires managing asynchronous tasks; if your workflow is simple and never involves long-running media files, you can simplify by only using the synchronous endpoint calls like moderate_text.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Hive AI. 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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Moderating user content shouldn't require a full compliance team.
Right now, if a user uploads an image or posts controversial text, your process is manual. You have to build dashboards, write custom webhooks, and coordinate multiple services just to get a simple 'safe/unsafe' flag. It's slow, expensive, and prone to failure.
With the Hive AI MCP Server, you let your agent do the heavy lifting. Instead of clicking through three different compliance panels, one tool call (`moderate_image` or `moderate_text`) gives you a clean score—a definitive 'go' or 'block.'
Hive AI MCP Server: Detect and filter content with precision.
Manual checks for images, audio, and video are nightmares. You have to manage different endpoints, handle file uploads differently, and stitch together results from multiple services just to know if the source is clean or flagged.
Now, you treat all content moderation like a single API call through your agent. Whether it's text (`moderate_text`) or video (`moderate_video_async`), the result—a confidence score and status—is delivered directly into your workflow.
Common Questions About Hive AI MCP
How do I find my Hive AI API Keys? +
Log in to your Hive AI dashboard, select a project (e.g., a Visual or Text project), and navigate to the Settings or API section. Note that each project type requires its own unique API key.
What is the difference between Synchronous and Asynchronous moderation? +
Synchronous moderation (moderate_text, moderate_image) returns results instantly and is best for real-time interactions. Asynchronous moderation (moderate_video_async) is used for larger files and requires polling the task status or using a callback.
Can this integration detect deepfakes or AI-generated voices? +
Yes! Hive AI provides specialized models for detecting AI-generated content across text, images, video, and audio. Use the detect_ai_generated_text and detect_ai_generated_image tools for these checks.
Is the integration secure for sensitive content? +
Absolutely. The integration uses official Hive AI Token authentication over HTTPS. Your credentials and analyzed content are encrypted and stored securely within the Vinkius Cloud infrastructure.
How do I check if an asynchronous task failed using `get_async_task_status`? +
The response status field tells you if a job succeeded or failed. If the status is 'FAILED', examine the error message returned to pinpoint the issue, which usually points to invalid input or quota limits.
What configuration data does `get_project_details` return for my Hive AI project? +
It pulls all current settings and metadata specific to your account. This includes project identifiers, active model versions, and any custom thresholds you've set up in the visual or text projects.
Are there rate limits when I use `moderate_text` repeatedly? +
Yes, usage is governed by quotas; hitting maximum calls requires waiting. If you run into throttling, implement an exponential backoff strategy before retrying your moderation requests.
What's the functional difference between `detect_ai_generated_text` and using `moderate_text`? +
They check different things entirely. The detection tool only gives a probability score on AI origin (e.g., GPT-4). The moderation tool analyzes the text against safety policies for hate speech, violence, or compliance issues.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
Cohere
Access Cohere AI models via API — chat with Command models, generate embeddings, rerank documents and tokenize text from any AI agent.
Redis Vector
Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.
Stability AI
Connect your AI to Stability AI's powerful image generation models. Create, upscale, and edit high-quality images directly and efficiently.
You might also like
Atlassian (Jira & Confluence)
The ultimate AI bridge for Jira and Confluence — manage projects, agile boards, and wikis via AI.
GoTab
Manage your restaurant operations — menus, orders, and tabs via AI.
Lacework (Cloud Security & CNAPP)
Secure your cloud via Lacework — search security alerts, monitor vulnerabilities, and audit cloud asset inventory.