Ollama MCP, Ready to Go
Connect your Ollama instance to Claude or Cursor using this MCP to run local LLMs and generate vector embeddings for your private data.
No credit card required. Experience the power of this integration risk-free.
Run local LLMs and generate vector embeddings for your private data.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Ollama MCP Server?
Average time for the server to become ready for requests over the last 14 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Ollama MCP: 12 Tools for Local LLM Inference
Use these tools to run local models, generate embeddings, and monitor your VRAM usage directly from your AI agent to keep your data private.
Chat
Send multimodal messages and get structured JSON responses from your models. It handles images and tool calling in one go.
Generate embeddings
Generate vector embeddings from single or multiple text inputs. This is perfect for building your own RAG pipelines.
Generate
Generate text completions with full control over parameters like seed and temperature. It supports structured JSON outputs too.
Get version
Retrieve the version of your local environment. Use this to ensure compatibility with your current tools.
List models
Get a full list of all models you have downloaded. Use this to see what's ready to run.
List running models
See which models are currently loaded in your VRAM. It shows the model size and when they'll be unloaded.
Openai chat completions
Generate chat completions using an OpenAI-compatible endpoint. It supports vision and tool calling.
Openai completions
Use the OpenAI-compatible completion endpoint for your existing workflows. It supports various stop sequences.
Openai embeddings
Generate embeddings via an OpenAI-compatible endpoint. It's a drop-in replacement for standard embedding calls.
Openai list models
Get model IDs and ownership info using the OpenAI-compatible list endpoint. This is great for existing SDK users.
Openai responses
Use the OpenAI-compatible Responses API to generate full responses. It works with your existing OpenAI-style logic.
Show model details
Get the deep specs for a specific model. You can check parameters, quantization, and tokenizer settings here.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,800+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Ollama MCP for Local LLM Inference and Privacy
This is for the developer who needs to keep data local, the AI engineer testing specific weights, or the data scientist building a custom RAG pipeline.
AI Engineer
Testing model outputs and comparing performance across different local weights on a Tuesday afternoon.
Data Scientist
Generating embeddings for high-volume vector databases for a new search feature.
Privacy-Focused Developer
Running sensitive tasks on-premise to ensure no data ever leaves the local machine.
Frequently Asked Questions
How do I get my Ollama API key? +
Visit the Ollama API keys page to create or manage your API key. Once created, copy it and paste it into the API key field in the setup wizard. The key is used as a Bearer token in the Authorization header for all requests to https://ollama.com/api. API keys don't currently expire, but you can revoke them at any time.
Which models are available through this MCP? +
All models available on ollama.com are accessible, including Gemma, GPT-OSS, Qwen, Llama, and many others. Use list_models to see all available models, and show_model_details to inspect a model's capabilities (completion, vision, tools), parameter size, quantization level, and architecture details.
Can my AI use vision models to analyze images? +
Yes! The generate and chat tools support image inputs as base64-encoded strings. Use a vision-capable model (check with show_model_details — look for 'vision' in capabilities) and pass images in the request. The OpenAI-compatible openai_chat_completions tool also supports image_url content parts for vision models.
What is the difference between native and OpenAI-compatible endpoints? +
The native Ollama endpoints (generate, chat, embed) offer the full feature set including thinking mode, keep_alive control, and detailed response metadata (token counts, durations). The OpenAI-compatible endpoints (/v1/chat/completions, /v1/completions, /v1/embeddings, /v1/responses) provide drop-in compatibility for existing applications built with the OpenAI SDK — they use the same request/response format as OpenAI's API.
Does this MCP support tool calling (function calling)? +
Yes. The chat tool supports tool calling — pass a JSON array of tool definitions in the tools_json parameter. The model will return tool calls in the response when it determines a function should be invoked. The OpenAI-compatible openai_chat_completions tool also supports tools via the tools_json parameter.
Can I generate structured JSON output? +
Yes. Both generate and chat tools support structured outputs via the format_json parameter. You can pass the string 'json' for basic JSON mode, or a full JSON schema object to enforce a specific output structure. The OpenAI-compatible endpoints also support response_format for structured output.
What is the base URL for API calls? +
All API calls are sent to https://ollama.com/api with the Authorization: Bearer YOUR_API_KEY header. The engine automatically appends the correct path for each endpoint (e.g. /generate, /chat, /v1/chat/completions). This MCP targets the Ollama cloud API — for local Ollama instances, the base URL would be http://localhost:11434/api (no API key required).
Your AI, connected to everything.
No credit card required · Free tier available
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