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/harness-sdk/tree/main/strands-mcp) 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.

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

(( tab "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 "Python" ))

(( tab "TypeScript" ))
```typescript
const mcpClient = new McpClient({
  transport: new StdioClientTransport({
    command: 'uvx',
    args: ['strands-agents-mcp-server'],
  }),
})

const agent = new Agent({ tools: [mcpClient] })
await agent.invoke('How do I create a custom tool in Strands Agents?')

await mcpClient.disconnect()
```
(( /tab "TypeScript" ))

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

(( tab "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 "Kiro" ))

(( tab "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 "Claude Code" ))

(( tab "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 "Cursor" ))

(( tab "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 "VS Code" ))

(( tab "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 "Other" ))

### 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`](/llms.txt) | Index file with links to all documentation pages in raw markdown format |
| [`/llms-full.txt`](/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](/docs/examples/index.md) for common patterns like [multi-agent systems](/docs/examples/python/multi_agent_example/multi_agent_example/index.md), [structured output](/docs/examples/structured_output/index.md), and [tool use](/docs/examples/python/mcp_calculator/index.md).
-   **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.

## Related pages

- [Model Context Protocol (MCP) Tools](/docs/user-guide/concepts/tools/mcp-tools/index.md) (2 shared tags)
- [Tools Overview](/docs/user-guide/concepts/tools/index.md) (2 shared tags)
- [Community Built Tools](/docs/user-guide/concepts/tools/community-tools-package/index.md) (1 shared tag)
- [Creating Custom Tools](/docs/user-guide/concepts/tools/custom-tools/index.md) (1 shared tag)
- [Vended Tools](/docs/user-guide/concepts/tools/vended-tools/index.md) (1 shared tag)
- [OpenAI Responses API](/docs/user-guide/concepts/model-providers/openai-responses/index.md) (1 shared tag)
- [Agents as Tools with Strands Agents SDK](/docs/user-guide/concepts/multi-agent/agents-as-tools/index.md) (1 shared tag)
- [Agent Configuration](/docs/user-guide/concepts/experimental/agent-config/index.md) (1 shared tag)