LocalAI MCP. Run Multimodal AI on Your Hardware.
LocalAI lets you run powerful AI models—including text chat, image generation, audio transcription, and face analysis—entirely on your own hardware. It provides a standard API endpoint compatible with OpenAI and Anthropic protocols, letting any client connect to private local models without sending sensitive data to the cloud.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
You generate text responses for chat or completions using local language models that support both OpenAI and Anthropic standards.
You prompt the system to synthesize unique images from scratch, even allowing you to define negative prompts to exclude unwanted elements.
You convert spoken audio into written text using transcription or generate natural-sounding speech files from plain text.
You verify a person's identity by comparing faces one-to-one, enroll new individuals, or detect objects within an image for analysis.
You generate vector embeddings to index text and use those vectors to improve search results based on a specific query.
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What AI agents can do with LocalAI: 20 Tools for Local AI Inference
These tools allow your agent to perform everything from generating chat responses and creating images to analyzing faces and transcribing audio, all using models running on your private hardware.
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 LocalAI MCPAnthropic Messages
Generates multi-turn chat messages using local models compatible with Anthropic’s API structure.
Apply Model
Installs a new AI language or media model from the available gallery.
Chat Completions
Generates conversational text responses using local models compatible with OpenAI’s...
Create Embeddings
Converts blocks of text into numerical vector embeddings for advanced search and...
Detect Objects
Scans an image and returns a list of identified objects along with their locations.
Face Analyze
Provides demographic or characteristic analysis on human faces found in images.
Face Identify
Compares a face to previously registered individuals to determine who the person is (1:N comparison).
Face Register
Enrolls and securely stores a new individual's facial data for future identification.
Face Verify
Confirms if an unknown face matches a known identity by comparing it one-to-one.
Generate Image
Creates entirely new visual content based on your text prompts, supporting negative...
Get Auth Status
Checks the current authentication status and lists available identity providers.
Get Auth Usage
Displays usage metrics for personal API tokens or access keys.
Get System Info
Retrieves general operational details and backend information about the local AI instance.
Get Version
Returns the specific version number of the LocalAI software running on the...
List Models
Retrieves a list of all AI models that are currently installed and ready for use by...
Open Responses
Generates open-ended, unstructured text responses when specific chat protocols...
Rerank Documents
Refines search results by reordering documents based on how closely they relate to...
Text To Speech
Converts plain text into an audio file using high-quality synthetic voice generation (TTS).
Transcribe Audio
Transcribes recorded speech files or paths, converting the spoken word back into editable text.
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 LocalAI 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 each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with LocalAI, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LocalAI. 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 CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Manual media pipelines are slow and expensive.
Today, generating marketing assets means passing text through a web form, downloading an image file, checking the resolution on Photoshop, writing a summary in Notion, and then uploading that document to your shared drive. It's click-by-click, manual copy-pasting that eats up hours of labor every week.
With this MCP, you simply tell your agent what you need—say, 'Generate five images of a futuristic library.' The system handles the generation using `generate_image`, and then it can automatically summarize the findings for your internal wiki. You get results in one controlled flow, without leaving your private network.
Get LocalAI's multimodal power with chat_completions
The biggest time sinks are the data transfers: recording a meeting, uploading it to a service, waiting for transcription, downloading the text file, and then pasting that text into another tool for summarization. It's a chain of manual handoffs.
Now, you pass the audio directly through the MCP using `transcribe_audio`, and your agent gets the clean text instantly. You can feed that output immediately to `chat_completions` for summarizing or even use it in `create_embeddings` for instant indexing. The whole process runs as one continuous, private operation.
What LocalAI MCP does for your AI
This MCP lets you bring advanced artificial intelligence capabilities right into your local environment. Instead of relying on third-party services for every single task, you can run powerful multimodal models directly from your own infrastructure. This means keeping all your sensitive data private while still accessing top-tier AI performance.
Whether you need to generate complex images from text prompts, convert recorded speech into searchable text, or analyze faces for identity verification, this connector handles it locally. You connect your preferred agent through Vinkius and gain access to a comprehensive set of tools that span everything from basic chat completions using chat_completions to advanced functions like generating vector embeddings with create_embeddings.
It's about giving you full control over where the AI processing happens, ensuring speed and privacy are always priorities.
019e38ba-2e24-73ee-8a88-40849fef4982 
How to set up LocalAI MCP
The bottom line is that you treat your private, locally hosted AI instance exactly like a cloud API endpoint from anywhere in the Vinkius catalog.
Subscribe to this MCP, providing your LocalAI Base URL (e.g., http://localhost:8080) and an optional API Key.
Your AI client connects using the provided credentials, establishing a secure link to your local models.
You interact with the system through your agent, triggering actions like text generation or image synthesis as if it were any other online service.
Who uses LocalAI MCP
This MCP is for developers and researchers who cannot send proprietary or sensitive data to third-party servers. It’s perfect for internal tools, compliance departments, and anyone building complex AI pipelines that demand absolute privacy.
You build client-facing applications handling personal data (like biometric information or private communications) and need to ensure the LLM processing never leaves the local machine.
You test out multiple open-source models for chat, vision, or audio tasks. This MCP lets you easily swap between different local model versions without changing your core code.
You integrate AI capabilities into internal automation pipelines, needing a stable, self-hosted endpoint that doesn't rely on external cloud service uptime or cost limits.
Benefits of connecting LocalAI MCP
Data Privacy: By running everything locally, you eliminate the risk of sending proprietary or sensitive data to any third-party cloud vendor. This is non-negotiable for compliance and internal tools.
Control Over Models: You maintain full control over which AI model runs your workflows. Need to test a new open-source LLM? Just apply it locally with apply_model and start using it immediately.
Full Media Pipeline: This MCP covers the whole stack. Generate images with generate_image, transcribe audio with transcribe_audio, and then convert summaries back into voice using text_to_speech—all without an internet dependency.
Advanced Search: Go beyond basic keyword searches. Use create_embeddings to index your documents, and then use rerank_documents to guarantee the most contextually relevant answers for RAG workflows.
Biometric Capabilities: Handle identity management securely. You can run specific tools like face_register or face_verify to process sensitive biometric data entirely on private hardware.
LocalAI MCP use cases
Compliance Auditing for Biometrics
An HR department needs a tool that verifies employee identities using photos taken at different sites. Instead of sending images offsite, they connect the MCP and use face_verify to perform 1:1 biometric checks entirely within their private network.
Creating Localized Marketing Assets
A marketing team needs dozens of unique product mockups for a campaign. They send a text description to the agent, which then uses generate_image to output high-res visuals without incurring massive cloud API costs.
Building Internal Call Summaries
A sales team records client calls on internal VoIP systems. They connect the MCP and use transcribe_audio immediately, then pass the resulting text to chat_completions to generate structured follow-up summaries for CRM entry.
Improving Knowledge Base Search
A legal firm has thousands of documents. Instead of just searching by keyword, they use create_embeddings across their entire corpus and then employ rerank_documents to ensure the agent retrieves the single most contextually relevant passage for a query.
LocalAI MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Using it only for basic chat
Thinking that since you can use chat_completions, you don't need to worry about data privacy. You might send your company's most sensitive documents through a general-purpose endpoint.
If the primary concern is just chatting, ensure the connection is local via this MCP. But remember, for anything involving media or biometrics, you must use dedicated tools like detect_objects and face_verify to keep the process contained.
Ignoring audio source requirements
Attempting to process a live microphone stream directly through the API endpoint. The system expects files or paths, not continuous streams.
For accurate speech processing, you must first capture and save the audio data (a file or path), then pass that specific file reference to transcribe_audio.
Thinking it replaces all APIs
Assuming this MCP can handle every single API call your organization uses, even those outside of AI, like database lookups or email sending.
This MCP is specifically for running LLM and media tools locally. For actions outside the scope of text, image, audio, or face analysis, you'll need a different integration.
When to use LocalAI MCP
Use this if your primary requirement is data sovereignty—if sending data to a third-party cloud provider violates privacy rules or costs too much. This MCP gives you the power of multimodal AI while keeping the processing local. Don't use it if you simply need a quick, one-off test using a publicly available online demo; for those, a simple public endpoint might be faster. However, if your workflow involves biometrics (face_verify), generating high volumes of media (generate_image), or processing sensitive audio, this local solution is mandatory. If your job only requires basic text completion without needing to reference private documents, you might just use a standard chat client, but for anything involving data indexing, go with the create_embeddings and rerank_documents tools here.
Frequently asked questions about LocalAI MCP
How do I start using LocalAI with chat_completions? +
You first connect your client to this MCP and ensure you have a local LLM installed via apply_model. Then, your agent can call the chat_completions tool just like it would any other API.
Can I run image generation if my data needs to stay private? +
Yes. By using the MCP, you leverage local models for media creation. You simply call generate_image, and the visual content is processed entirely on your own hardware.
What's the difference between face_identify and face_verify? +
Face verification (face_verify) confirms if a single unknown face matches a known person (1:1). Face identification (face_identify) determines who a person is by comparing their face against many registered identities (1:N).
Does LocalAI help me search my documents better? +
Absolutely. Instead of basic keyword searches, you use create_embeddings to build searchable vectors from your documents and then use rerank_documents to improve the relevance of retrieved results.
How do I make sure my audio files are processed correctly? +
You must first pass the file path or raw data through the transcribe_audio tool. This converts the speech into text, which you can then use with any of the other chat tools.