---
title: Build with AI
tags: [mcp, tool-authoring]
description: Use AI coding assistants to build Strands Agents projects with up-to-date documentation context.
redirectFrom:
  - docs/llms
---

AI coding assistants work best when they have access to current documentation. Strands Agents provides two ways to give your AI tools the context they need: an **MCP server** for interactive documentation search, and **llms.txt files** for bulk documentation access.

## Strands Agents MCP Server

The [Strands Agents MCP server](https://github.com/strands-agents/mcp-server) gives AI coding assistants direct access to the Strands Agents documentation through the [Model Context Protocol (MCP)](https://modelcontextprotocol.io). It provides intelligent search with TF-IDF based ranking, section-based browsing for token-efficient retrieval, and on-demand content fetching so your AI tools can find and retrieve exactly the documentation they need.

### Prerequisites

The MCP server requires [uv](https://github.com/astral-sh/uv) to be installed on your system. Follow the [official installation instructions](https://github.com/astral-sh/uv#installation) to set it up.

### Setup

Choose your AI coding tool below and follow the setup instructions.

<Tabs>
<Tab label="Strands">

You can use the Strands Agents MCP server as a tool within your own Strands agents:

<Tabs>
<Tab label="Python">

```python
from mcp import stdio_client, StdioServerParameters
from strands import Agent
from strands.tools.mcp import MCPClient

mcp_client = MCPClient(lambda: stdio_client(
    StdioServerParameters(
        command="uvx",
        args=["strands-agents-mcp-server"]
    )
))

agent = Agent(tools=[mcp_client])
agent("How do I create a custom tool in Strands Agents?")
```

</Tab>
<Tab label="TypeScript">

```typescript
--8<-- "user-guide/build-with-ai.ts:mcp_strands"
```

</Tab>
</Tabs>

See the [MCP tools documentation](/docs/user-guide/concepts/tools/mcp-tools/) for more details on using MCP tools with Strands agents.

</Tab>
<Tab label="Kiro">

Add the following to `~/.kiro/settings/mcp.json`:

```json
{
  "mcpServers": {
    "strands-agents": {
      "command": "uvx",
      "args": ["strands-agents-mcp-server"],
      "disabled": false,
      "autoApprove": ["search_docs", "fetch_doc"]
    }
  }
}
```

See the [Kiro MCP documentation](https://kiro.dev/docs/mcp/configuration/) for more details.

</Tab>
<Tab label="Claude Code">

Run the following command:

```bash
claude mcp add strands uvx strands-agents-mcp-server
```

See the [Claude Code MCP documentation](https://docs.anthropic.com/en/docs/claude-code/tutorials#configure-mcp-servers) for more details.

</Tab>
<Tab label="Cursor">

Add the following to `~/.cursor/mcp.json`:

```json
{
  "mcpServers": {
    "strands-agents": {
      "command": "uvx",
      "args": ["strands-agents-mcp-server"]
    }
  }
}
```

See the [Cursor MCP documentation](https://docs.cursor.com/context/model-context-protocol#configuring-mcp-servers) for more details.

</Tab>
<Tab label="VS Code">

Add the following to your `mcp.json` file:

```json
{
  "servers": {
    "strands-agents": {
      "command": "uvx",
      "args": ["strands-agents-mcp-server"]
    }
  }
}
```

See the [VS Code MCP documentation](https://code.visualstudio.com/docs/copilot/customization/mcp-servers) for more details.

</Tab>
<Tab label="Other">

The Strands Agents MCP server works with [40+ applications that support MCP](https://modelcontextprotocol.io/clients). The general configuration is:

- **Command:** `uvx`
- **Args:** `["strands-agents-mcp-server"]`

</Tab>
</Tabs>

### Verify the connection

You can test the MCP server using the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector):

```bash
npx @modelcontextprotocol/inspector uvx strands-agents-mcp-server
```

## llms.txt files

The Strands Agents documentation site provides [llms.txt](https://llmstxt.org/) files optimized for AI consumption. These are static files containing the full documentation in plain markdown, suitable for feeding directly into an LLM's context window.

### Available endpoints

| Endpoint | Description |
|----------|-------------|
| [`/llms.txt`](https://strandsagents.com/llms.txt) | Index file with links to all documentation pages in raw markdown format |
| [`/llms-full.txt`](https://strandsagents.com/llms-full.txt) | Complete documentation content in a single file (excludes API reference) |

### Raw markdown convention

Every documentation page is available in raw markdown format by appending `/index.md` to its URL path:

- [`/docs/user-guide/quickstart/`](https://strandsagents.com/docs/user-guide/quickstart/) → [`/docs/user-guide/quickstart/index.md`](https://strandsagents.com/docs/user-guide/quickstart/index.md)
- [`/docs/user-guide/concepts/tools/`](https://strandsagents.com/docs/user-guide/concepts/tools/) → [`/docs/user-guide/concepts/tools/index.md`](https://strandsagents.com/docs/user-guide/concepts/tools/index.md)

This gives you clean markdown content without HTML markup, navigation, or styling.

### When to use llms.txt

The llms.txt files are useful when:

- Your AI tool does not support MCP
- You want to provide full documentation context in a single prompt
- You are building custom tooling around the documentation

:::note
The llms-full.txt file contains the entire documentation and can be large. For most use cases, the MCP server provides a more token-efficient way to access documentation.
:::

## Tips for AI-assisted Strands development

- **Use the MCP server over llms.txt when possible** — it retrieves only the relevant sections, saving tokens and improving accuracy.
- **Start from examples** — point your AI tool at the [examples](../examples/README.mdx) for common patterns like [multi-agent systems](../examples/python/multi_agent_example/multi_agent_example.mdx), [structured output](../examples/structured_output.mdx), and [tool use](../examples/python/mcp_calculator.mdx).
- **Review AI-generated code** — always verify that generated code follows the patterns in the official documentation, especially for model provider configuration and tool definitions.
- **Use project rules** — many AI coding tools support project-level instructions (e.g., `.cursorrules`, `CLAUDE.md`). Add Strands-specific conventions to keep AI output consistent across your project.
