Eden AI MCP for AI. One API for every model and task.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Connect to your AI in seconds.
Eden AI routes requests across over 100 AI models, letting you use multiple providers—like OpenAI, Anthropic, or Google—from one place.
It handles everything from standard chat completions to specialized tasks like OCR and translating images, all without juggling dozens of API keys.
What your AI can do
Check credits
Retrieves your current credit balance for Eden AI, letting you know if you have enough funds to run a job.
List files
Lists every file you have uploaded to the MCP's storage, helping you track what context is available.
Chat completions
Creates a standard conversational response by routing the request to the desired AI model or using smart routing logic.
You can initiate conversations and specify which AI model should handle the response without changing your code.
It executes dedicated, non-chat jobs like Optical Character Recognition (OCR), image generation, or translating text across formats.
You can upload documents to persistent storage and list them later so your AI agent has context for its tasks.
The MCP converts raw text into numerical vectors, which is essential for building advanced search or retrieval systems.
You check your remaining credits and monitor overall API consumption to keep a tight grip on costs.
Ask an AI about this
Waiting for input…
Eden AI Alternative: 13 Tools
These tools let you run complex multi-step workflows by controlling model routing, generating vectors, managing files, or executing specialized tasks like translation.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Eden AI on VinkiusCheck Credits
Retrieves your current credit balance for Eden AI, letting you know if you have enough funds to run a job.
List Files
Lists every file you have uploaded to the MCP's storage, helping you track what...
Chat Completions
Creates a standard conversational response by routing the request to the desired AI...
Create Custom Token
Generates a unique API token that can be restricted by specific usage limits or...
Create Embedding
Converts blocks of text into numerical vectors, which are necessary for semantic...
Create Stateful Response
Creates a chat history that the MCP stores on its side, allowing conversations to continue over multiple calls.
Delete Files
Removes files you previously uploaded from Eden AI's persistent storage for cleanup purposes.
Get Async Job
Checks the status and retrieves results from a job that ran in the background, like...
List Embedding Models
Provides a list of all available models designed specifically for creating text...
Monitor Consumption
Provides detailed data on your API usage and costs, so you can track spending over...
Universal Ai Async
Starts a background job for complex tasks like Speech-to-Text or large image...
Universal Ai Sync
Executes instant, specialized tasks like translating text or running OCR directly when you need an immediate result.
Upload File
Uploads a document or file to the MCP's persistent storage so your AI agent can reference it later.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Eden AI integration is available immediately — no restart needed.
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 Eden AI, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Eden 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 connection provides 13 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with AI APIs used to feel like a patchwork quilt.
Today, if you build an agent that needs to read a document, translate it, and then summarize it, your development stack looks like three different companies' SDKs. You manage keys for OpenAI, Anthropic, and perhaps Google Cloud just for the file storage part. Every time one company updates its API endpoint or changes its pricing model, you have to drop everything and rewrite your code.
With this MCP, that complexity vanishes. You interact with a single unified entry point. Whether the underlying power comes from GPT-4, Claude 3, or another provider, your agent just makes one call. It's stable, it’s simple, and you get the full picture of what happens when the job runs.
The `universal_ai_sync` tool lets you process media types instantly.
Previously, if your workflow required extracting text from an image or translating a foreign language document, it involved separate services: one for the vision model and another for the translation endpoint. You had to pass data back and forth between them manually.
Now, you use `universal_ai_sync`. It handles the entire sequence—image input, OCR extraction, and clean text output—in a single operation. The process is cleaner, faster, and far less prone to broken handoffs.
What your AI can actually do with this
Managing a multi-model stack is usually a nightmare; every time you want to add a new feature, you write custom code for OpenAI, then rewrite it again for Claude, then update the whole thing for Google. This MCP solves that by providing one unified endpoint for everything. You stop managing API keys and start building workflows.
Need to chat with GPT-4 but use Anthropic's model for summarization? No problem. It handles the routing automatically. Beyond standard conversations, you can execute specialized tasks like converting audio to text or generating images using dedicated tools. Plus, it keeps track of your usage so you know exactly what you’re spending.
When you connect this MCP via Vinkius, you get a single access point to run complex pipelines regardless of which provider owns the underlying AI brain.
019e5d16-04bc-7147-be92-4601b33d50c3 
Here's how it actually works
The bottom line is you talk to one address, and it figures out where the answer needs to come from.
Subscribe to this MCP and provide your unique Eden AI API Key.
Your agent calls the unified endpoint, specifying the required model type (e.g., GPT-4 or Claude 3) and task (e.g., chat completion).
The MCP routes the request internally, executes the job with the correct provider, and returns the structured result to your client.
Who is this actually for?
This MCP is for platform engineers and data scientists who build complex automation pipelines. If your current job involves calling APIs from three or more different AI companies (OpenAI, Google, etc.) just to complete one workflow, you need this.
Builds workflows that automatically pull data from a document, translate it using a specific model, and then summarize the results for logging.
Needs to compare embedding quality across multiple providers (like comparing create_embedding output) before committing to one vendor for RAG systems.
Builds internal tools that require handling different AI model types—sometimes a chat, sometimes image generation—all through a single, stable API interface.
What Changes When You Connect
Don't write different integration code just to support Claude or Google. The chat_completions tool handles routing, meaning your application logic stays clean no matter which provider powers the chat.
You get full control over resource costs by checking credits first. Use check_credits and monitor_consumption together so you never run a job only to hit an unexpected bill.
It simplifies specialized tasks. Instead of connecting separate services for OCR, translation, or image generation, the universal_ai_sync tool handles them all under one roof.
The MCP manages your data context. Use upload_file and list_files to give your agent specific documents it needs to read before running a summary task.
Handle heavy lifting without blocking your user interface. The universal_ai_async tool kicks off long jobs (like transcribing hours of audio) and you just poll the status with get_async_job later.
See it in action
Analyzing a mixed-format corporate presentation
A user uploads a PDF report using upload_file. The agent then runs OCR on it via universal_ai_sync, creates embeddings with create_embedding to index the text, and finally generates a summary chat response using chat_completions based only on that indexed data.
Building an automated customer support triage bot
The agent receives a voice note from a user. It uses universal_ai_async to transcribe the audio, then sends the resulting text and file context via create_stateful_response. The chat model can then analyze the intent without needing multiple API keys for transcription and chat.
Testing LLM performance before launch
A development team needs to compare GPT-4’s summarization against Claude 3.5's summary output. They use chat_completions multiple times in a single test run, guaranteeing they are comparing apples to apples using the same routing mechanism.
Generating and indexing knowledge bases
A data scientist uploads dozens of PDFs. They list them with list_files, then use create_embedding on chunks of text from those files, building a reliable vector index for semantic search.
The honest tradeoffs
Manually linking services
Trying to connect separate APIs (one for OpenAI chat, one for Google OCR, and a third for file storage) means you have to write complex authentication and error handling code for every single integration point.
Instead, use the MCP's unified approach. Upload your data with upload_file, then run specialized tasks like OCR using universal_ai_sync before sending the result to a chat completion call.
Forgetting about asynchronous jobs
Attempting to process an hour-long audio file with a synchronous API call causes your entire client application or agent to time out and fail.
Always use universal_ai_async for heavy tasks. This kicks off the job in the background, allowing you to check its status later using get_async_job.
Ignoring cost tracking
Running high-volume embedding generation (create_embedding) without checking your budget until the end of the month.
Always start by running check_credits and keep an eye on monitor_consumption. It keeps you in control of your spend.
When It Fits, When It Doesn't
Use this MCP if your project requires a blend of services: chat, image generation, translation, and file handling. If you are building any workflow that needs more than one type of AI service—say, reading an image (OCR) and then summarizing the text (LLM)—this is mandatory. Don't use it if all you need is a simple, single-purpose chat query from one vendor; in that case, connecting directly to that vendor's native API might be marginally simpler. However, because of its unified nature, this MCP provides immediate flexibility and risk mitigation by centralizing the API key management layer.
Questions you might have
How do I use `chat_completions` if I want to switch between GPT-4 and Claude? +
You specify the desired provider or model within your call parameters for chat_completions. The MCP handles the routing, so you don't need separate code blocks for each LLM.
Does using `universal_ai_async` mean my job runs forever? +
No. You use get_async_job to check on its status and retrieve the final result once it's done processing, so you know exactly when the background task finished.
`create_embedding` only works with text input? +
No. While list_embedding_models shows available options, create_embedding handles converting structured data and text into vectors for use in search applications.
What's the difference between `universal_ai_sync` and `chat_completions`? +
Sync tools handle specialized tasks like OCR or translation immediately. Chat completions are designed specifically for conversational turns, maintaining history via create_stateful_response.
I want to improve security; how do I use `create_custom_token`? +
You create a custom token with specific constraints. This limits the token's scope and permissions, which is great for reducing risk if credentials get compromised.
After I upload files using `upload_file`, how do I delete them from storage? +
You must explicitly call delete_files. This tool removes the data from Eden AI's persistent storage, ensuring you manage file lifecycle and clean up old context.
What is the difference between regular chat completions and using `create_stateful_response`? +
The stateful response stores conversation history on the server. This means your agent doesn't have to resend the full transcript every time; it just references the session ID.
If I use `check_credits`, can I also track my spending over a period of time? +
Yes, you run monitor_consumption. This tool gives you historical data and detailed cost breakdowns, helping you plan your API usage across different models.
How can I use Eden AI's smart routing to find the best model for a chat? +
Simply use the chat_completions tool and set the model parameter to @edenai. This will automatically route your request to the most suitable provider based on performance and cost.
How do I handle long-running AI tasks like Speech-to-Text? +
Use the universal_ai_async tool to start the job. You will receive a job ID which you can then use with the get_async_job tool to check the status and retrieve the final results once finished.
Can I manage the files I upload for AI processing? +
Yes. You can use upload_file to send data, list_files to see everything in your storage, and delete_files to remove them when they are no longer needed.
We've already built the connector for Eden AI. Just plug in your AI agents and start using Vinkius.
No hosting. No infrastructure. No complex setup.
All 13 tools are live and waiting.
You're up and running in seconds.
Gemini Vinkius gives your AI agents access to the full catalog of app connectors, all fully managed, secure, and enterprise-ready. One subscription, every tool you need.
Built, hosted, and secured by Vinkius. You just connect and go.