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‎units/en/unit2/continue-client.mdx‎

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# Using MCP with Local and Open Source Models
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In this section, we'll connect MCP with local and open-source models using
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Continue, a tool for building AI coding assistants that works with local tools
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like Ollama.
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## Setup Continue
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You can install Continue from the VS Code marketplace.
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*Note: Continue also has an extension for JetBrains.*
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### VS Code extension
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1. Click `Install` on the [Continue extension page in the Visual Studio Marketplace](https://marketplace.visualstudio.com/items?itemName=Continue.continue)
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2. This will open the Continue extension page in VS Code, where you will need to click `Install` again
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3. The Continue logo will appear on the left sidebar. For a better experience, move Continue to the right sidebar
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![](https://images.type.ai/img_9FWEqi6ArNvSvOLbCB.gif)
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With Continue configured, we'll move on to setting up Ollama to pull local models.
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### Ollama local models
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Ollama is an open-source tool that allows users to run large language models (LLMs)
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locally on their own computers. To use Ollama, you can [install](https://ollama.com/download) it and
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download the model you want to run with the `ollama run` command.
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For example, you can download the [llama 3.1:8b](https://ollama.com/models/llama-3:1b) model with:
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```bash
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ollama pull llama3.1:8b
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```
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It is important that we use models that have tool calling as a built-in feature, such as Codestral and Llama 3.1x.
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1. Create a folder called `.continue/models` at the top level of your workspace
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2. Add a file called `llama-max.yaml` to this folder
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3. Write the following contents to `llama-max.yaml` and save
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```yaml
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name: Ollama Llama model
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version: 0.0.1
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schema: v1
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models:
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- provider: ollama
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model: llama3.1:8b
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defaultCompletionOptions:
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contextLength: 50000
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name: a llama3.1:8b max
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roles:
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- chat
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- edit
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```
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By default, the max context length is `8192`. This setup includes a larger use of
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that context window to perform multiple MCP requests and also allows for more
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tokens to be used.
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## How it works
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### The tool handshake
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Tools provide a powerful way for models to interface with the external world.
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They are provided to the model as a JSON object with a name and an arguments
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schema. For example, a `read_file` tool with a `filepath` argument will give the
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model the ability to request the contents of a specific file.
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The following handshake describes how the Agent uses tools:
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1. In Agent mode, available tools are sent along with `user` chat requests
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2. The model can choose to include a tool call in its response
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3. The user gives permission. This step is skipped if the policy for that tool is set to `Automatic`
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4. Continue calls the tool using built-in functionality or the MCP server that offers that particular tool
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5. Continue sends the result back to the model
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6. The model responds, potentially with another tool call, and step 2 begins again
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Continue supports multiple local model providers. You can use different models
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for different tasks or switch models as needed. This section focuses on
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local-first solutions, but Continue does work with popular providers
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like OpenAI, Anthropic, Microsoft/Azure, Mistral, and more. You can also run
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your own model provider.
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### Local Model Integration with MCP
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Now that we have everything set up, let's add an existing MCP server. Below is a quick example of setting up a new MCP server for use in your assistant:
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1. Create a folder called `.continue/mcpServers` at the top level of your workspace
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2. Add a file called `playwright-mcp.yaml` to this folder
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3. Write the following contents to `playwright-mcp.yaml` and save
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```yaml
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name: Playwright mcpServer
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version: 0.0.1
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schema: v1
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mcpServers:
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- name: Browser search
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command: npx
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args:
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- "@playwright/mcp@latest"
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```
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Now test your MCP server by prompting the following command:
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```
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1. Using playwright, navigate to https://news.ycombinator.com.
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2. Extract the titles and URLs of the top 4 posts on the homepage.
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3. Create a file named hn.txt in the root directory of the project. 
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4. Save this list as plain text in the hn.txt file, with each line containing the title and URL separated by a hyphen.
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Do not output code or instructions—just complete the task and confirm when it is done.
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```
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The result will be a generated file called `hn.txt` in the current working directory.
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![](https://images.type.ai/img_o3z9i3HnFJNfCm9bgu.png)
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## Conclusion
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##
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By combining Continue with local models like Llama 3.1 and MCP servers, you've
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unlocked a powerful development workflow that keeps your code and data private
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while leveraging cutting-edge AI capabilities.
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This setup gives you the flexibility to customize your AI assistant with
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specialized tools, from web automation to file management, all running entirely
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on your local machine. Ready to take your development workflow to the next
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level? Start by experimenting with different MCP servers from the [Continue Hub
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MCP explore page](https://hub.continue.dev/explore/mcp) and discover how
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local AI can transform your coding experience.

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