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Build agents and prompts in AI Toolkit

Note

Agent Builder was previously known as Prompt Builder. The updated name better reflects the feature's capabilities and its focus on building agents.

Agent Builder in AI Toolkit streamlines the engineering workflow for building agents, including prompt engineering and integration with tools, such as MCP servers. It helps with common prompt engineering tasks:

  • Generate starter prompts
  • Iterate and refine with each run
  • Break down complex tasks through prompt chaining and structured outputs
  • Provide easy access to code for seamless Large Language Model (LLM) integration via APIs

Agent Builder also enhances intelligent app's capabilities with tool use:

  • Connect to existing MCP servers
  • Build new MCP servers from scaffolds
  • Use function calling to connect to external APIs and services

Screenshot showing the Agent Builder interface with prompt engineering and testing capabilities.

Create, edit, and test prompts

To access Agent Builder, use either of these options:

  • In the AI Toolkit view, select Agent Builder
  • Select Try in Agent Builder from a model card in the model catalog

To test a prompt in Agent Builder, follow these steps:

  1. In Models, select a model from the dropdown list, or select Browse models to add another model from the model catalog.

    Screenshot showing the model selection dropdown in Agent Builder.

  2. Enter a User prompt and optionally enter a System prompt.

    The user prompt is the input that you want to send to the model. The optional system prompt is used to provide instructions with relevant context to guide the model response.

    Tip

    Describe your project idea using natural language to generate prompts automatically. Screenshot showing natural language prompt generation in Agent Builder.

  3. Select Run to send the prompts to the model.

  4. Optionally, select Add Prompt to add more prompts or Add to Prompts to build conversation history.

  5. Repeat the previous steps to iterate over your prompts by observing the model response and making changes to the prompts.

Use MCP servers

MCP server is a tool that allows you to connect to external APIs and services, enabling your agent to perform actions beyond just generating text. For example, you can use an MCP server to access databases, call web services, or interact with other applications.

Use the agent builder to discover and configure featured MCP servers, connect to existing MCP servers, or build a new MCP server from scaffold.

Note

Using MCP servers might require either Node or Python environment. AI Toolkit validates your environment to ensure that the required dependencies are installed. After installing, use the command npm install -g npx to install npx. If you prefer Python, we recommend using uv

Configure a featured MCP server

AI Toolkit provides a list of featured MCP servers that you can use to connect to external APIs and services.

To configure an MCP server from featured selections, follow these steps:

  1. In the Tools section, select + MCP Server, and then select + Add server in the Quick Pick.
  2. Select Use Featured MCP Servers from the dropdown list.
  3. Choose an MCP server that meets your needs. Screenshot showing the connect to MCP server interface in Agent Builder.
  4. Enter a name for the server.
  5. Select tools you want to use. Screenshot showing the select tools interface in Agent Builder.

Use an existing MCP server

Tip

Find MCP servers in these reference servers.

To use an existing MCP server, follow these steps:

  1. In the Tools section, select + MCP Server, and then select + Add server in the quick pick.
  2. Select Connect to an Existing MCP Server
  3. Select an option from the dropdown list to specify how you want to connect to the MCP server:
    • Command (stdio): Run a local command that implements the MCP protocol
    • HTTP (server-sent events): Connect to a remote server that implements the MCP protocol
  4. Select tools from the MCP server if there are multiple tools available.
  5. Enter your prompts and select Run to test the connection.

Here's an example of configuring the Filesystem server in AI Toolkit:

  1. In the Tools section, select + MCP Server, and then select + Add server in the Quick Pick.

  2. Select Connect to an Existing MCP Server

  3. Select Command (stdio)

    Note

    Some servers use the Python runtime and the uvx command. The process is the same as using the npx command.

  4. Navigate to the Server instructions and locate the npx section.

  5. Copy the command and args into the input box in AI Toolkit. For the Filesystem server example, it's npx -y @modelcontextprotocol/server-filesystem /Users/<username>/.aitk/examples

  6. Input a name for the server.

  7. Optionally, enter extra environment variables. Some servers might require extra environment variables such as API keys. In this case, AI Toolkit fails at the stage of adding tools and a file mcp.json opens, where you can enter the required server details following the instructions provided by each server. Screenshot showing an example of a missing arg exception After you complete the configuration: 1. Navigate back to Tools section and select + MCP Server 1. Select the server you configured from the dropdown list

  8. Select the tools you want to use.

connect to MCP server

AI Toolkit also provides a scaffold to help you build a new MCP server. The scaffold includes a basic implementation of the MCP protocol, which you can customize to suit your needs.

Build a new MCP server

To build a new MCP server, follow these steps:

  1. In the Tools section, select + MCP Server, and then select + Add server in the quick pick.
  2. Select Create a New MCP Server
  3. Select a programming language from the dropdown list: Python or TypeScript
  4. Select a folder to create the new MCP server project in.
  5. Enter a name for the MCP server project.

Animated GIF showing how to use the scaffold mcp

After you create the MCP server project, you can customize the implementation to suit your needs. The scaffold includes a basic implementation of the MCP protocol, which you can modify to add your own functionality.

You can also use the agent builder to test the MCP server. The agent builder sends the prompts to the MCP server and displays the response.

Follow these steps to test the MCP server:

Note

To run the MCP Server in your local dev machine, you need: Node.js or Python installed on your machine.

  1. Open VS Code Debug panel. Select Debug in Agent Builder or press F5 to start debugging the MCP server.
  2. Use AI Toolkit Agent Builder to test the server with the following prompt:
    1. System Prompt: You are a weather forecast professional that can tell weather information based on given location.
  3. The server is automatically connected to Agent Builder.
  4. Select Run to test the server with the prompt.

Animated GIF showing the debug mcp process

Use function calling

Function calling connects your agent to external APIs and services.

Screenshot showing the Add Custom Function Tool dialog with options to add tools by example or upload schemas.

  1. In Tools, select Add Tool, then Custom Tool.
  2. Choose how to add the tool:
    • By Example: Add from a JSON schema example
    • Upload Existing Schema: Upload a JSON schema file
  3. Enter the tool name and description, then select Add.
  4. Provide a mock response in the tool card.

Screenshot showing a function calling tool card with weather tool configuration.

  1. Run the agent with the function calling tool.

Use function calling tools in the Evaluation tab by entering mock responses for test cases.

Screenshot showing function calling tool usage in the evaluation tab.

Structured output

Structured output support helps you design prompts to deliver outputs in a structured, predictable format.

Animated GIF showing how to use structured output

To test using structured output in Agent Builder, follow these steps:

  1. Select the Structure output from the left area, and select json_schema.

  2. Select Prepare schema, and then select Select local file to use your own schema, or select Use an example to use a predefined schema.

    If you proceed with an example, you can select a schema from the dropdown list.

  3. Select Run to send the prompts to the selected model.

  4. You can also edit the schema by selecting name of the schema.

    Screenshot showing the edit schema dropdown in Agent Builder.

Integrate prompt engineering into your application

After experimenting with models and prompts, you can get into coding right away with the automatically generated Python code.

Animated GIF showing the generated Python code

To view the Python code, follow these steps:

  1. Select View Code.

  2. For models hosted on GitHub, select the inference SDK you want to use.

    AI Toolkit generates the code for the model you selected by using the provider's client SDK. For models hosted by GitHub, you can choose which inference SDK you want to use: Azure AI Inference SDK or the SDK from the model provider, such as OpenAI SDK or Mistral API.

  3. The generated code snippet is shown in a new editor, where you can copy it into your application.

    To authenticate with the model, you usually need an API key from the provider. To access models hosted by GitHub, generate a personal access token (PAT) in your GitHub settings.

What you learned

In this article, you learned how to:

  • Use the AI Toolkit for VS Code to test and debug your agents.
  • Discover, configure, and build MCP servers to connect your agents to external APIs and services.
  • Set up function calling to connect your agents to external APIs and services.
  • Implement structured output to deliver predictable results from your agents.
  • Integrate prompt engineering into your application with generated code snippets.

Next steps

07/14/2025