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Version: v0.2.23

Tools

Tools are functions that can be invoked by an agent to perform tasks. They are organized into tool groups and registered with specific providers. Each tool group represents a collection of related tools from a single provider. They are organized into groups so that state can be externalized: the collection operates on the same state typically.

An example of this would be a "db_access" tool group that contains tools for interacting with a database. "list_tables", "query_table", "insert_row" could be examples of tools in this group.

Tools are treated as any other resource in llama stack like models. You can register them, have providers for them etc.

When instantiating an agent, you can provide it a list of tool groups that it has access to. Agent gets the corresponding tool definitions for the specified tool groups and passes them along to the model.

Refer to the Building AI Applications notebook for more examples on how to use tools.

Server-side vs. Client-side Tool Execution​

Llama Stack allows you to use both server-side and client-side tools. With server-side tools, agent.create_turn can perform execution of the tool calls emitted by the model transparently giving the user the final answer desired. If client-side tools are provided, the tool call is sent back to the user for execution and optional continuation using the agent.resume_turn method.

Server-side Tools​

Llama Stack provides built-in providers for some common tools. These include web search, math, and RAG capabilities.

You have three providers to execute the web search tool calls generated by a model: Brave Search, Bing Search, and Tavily Search.

To indicate that the web search tool calls should be executed by brave-search, you can point the "builtin::websearch" toolgroup to the "brave-search" provider.

client.toolgroups.register(
    toolgroup_id="builtin::websearch",
    provider_id="brave-search",
    args={"max_results": 5},
)

The tool requires an API key which can be provided either in the configuration or through the request header X-LlamaStack-Provider-Data. The format of the header is:

{"<provider_name>_api_key": <your api key>}

Math​

The WolframAlpha tool provides access to computational knowledge through the WolframAlpha API.

client.toolgroups.register(
    toolgroup_id="builtin::wolfram_alpha",
    provider_id="wolfram-alpha"
)

Example usage:

result = client.tool_runtime.invoke_tool(
    tool_name="wolfram_alpha",
    args={"query": "solve x^2 + 2x + 1 = 0"}
)

RAG​

The RAG tool enables retrieval of context from various types of memory banks (vector, key-value, keyword, and graph).

# Register Memory tool group
client.toolgroups.register(
    toolgroup_id="builtin::rag",
    provider_id="faiss",
    args={"max_chunks": 5, "max_tokens_in_context": 4096},
)

Features:

  • Support for multiple memory bank types
  • Configurable query generation
  • Context retrieval with token limits
Default Configuration

By default, llama stack run.yaml defines toolgroups for web search, wolfram alpha and rag, that are provided by tavily-search, wolfram-alpha and rag providers.

Model Context Protocol (MCP)​

MCP is an upcoming, popular standard for tool discovery and execution. It is a protocol that allows tools to be dynamically discovered from an MCP endpoint and can be used to extend the agent's capabilities.

Using Remote MCP Servers​

You can find some popular remote MCP servers here. You can register them as toolgroups in the same way as local providers.

client.toolgroups.register(
    toolgroup_id="mcp::deepwiki",
    provider_id="model-context-protocol",
    mcp_endpoint=URL(uri="https://mcp.deepwiki.com/sse"),
)

Note that most of the more useful MCP servers need you to authenticate with them. Many of them use OAuth2.0 for authentication. You can provide authorization headers to send to the MCP server using the "Provider Data" abstraction provided by Llama Stack. When making an agent call,

agent = Agent(
    ...,
    tools=["mcp::deepwiki"],
    extra_headers={
        "X-LlamaStack-Provider-Data": json.dumps(
            {
                "mcp_headers": {
                    "http://mcp.deepwiki.com/sse": {
                        "Authorization": "Bearer <your_access_token>",
                    },
                },
            }
        ),
    },
)
agent.create_turn(...)

Running Your Own MCP Server​

Here's an example of how to run a simple MCP server that exposes a File System as a set of tools to the Llama Stack agent.

# Start your MCP server
mkdir /tmp/content
touch /tmp/content/foo
touch /tmp/content/bar
npx -y supergateway --port 8000 --stdio 'npx -y @modelcontextprotocol/server-filesystem /tmp/content'

Adding Custom (Client-side) Tools​

When you want to use tools other than the built-in tools, you just need to implement a python function with a docstring. The content of the docstring will be used to describe the tool and the parameters and passed along to the generative model.

# Example tool definition
def my_tool(input: int) -> int:
    """
    Runs my awesome tool.

    :param input: some int parameter
    """
    return input * 2
Documentation Best Practices

We employ python docstrings to describe the tool and the parameters. It is important to document the tool and the parameters so that the model can use the tool correctly. It is recommended to experiment with different docstrings to see how they affect the model's behavior.

Once defined, simply pass the tool to the agent config. Agent will take care of the rest (calling the model with the tool definition, executing the tool, and returning the result to the model for the next iteration).

# Example agent config with client provided tools
agent = Agent(client, ..., tools=[my_tool])

Refer to llama-stack-apps for an example of how to use client provided tools.

Tool Invocation​

Tools can be invoked using the invoke_tool method:

result = client.tool_runtime.invoke_tool(
    tool_name="web_search",
    kwargs={"query": "What is the capital of France?"}
)

The result contains:

  • content: The tool's output
  • error_message: Optional error message if the tool failed
  • error_code: Optional error code if the tool failed

Listing Available Tools​

You can list all available tools or filter by tool group:

# List all tools
all_tools = client.tools.list_tools()

# List tools in a specific group
group_tools = client.tools.list_tools(toolgroup_id="search_tools")

Complete Examples​

Web Search Agent​

  1. Start by registering a Tavily API key at Tavily.
  2. [Optional] Provide the API key directly to the Llama Stack server
export TAVILY_SEARCH_API_KEY="your key"
--env TAVILY_SEARCH_API_KEY=${TAVILY_SEARCH_API_KEY}

WolframAlpha Math Agent​

  1. Start by registering for a WolframAlpha API key at WolframAlpha Developer Portal.
  2. Provide the API key either when starting the Llama Stack server: 
    --env WOLFRAM_ALPHA_API_KEY=${WOLFRAM_ALPHA_API_KEY}
    or from the client side: 
    client = LlamaStackClient(
        base_url="http://localhost:8321",
        provider_data={"wolfram_alpha_api_key": wolfram_api_key},
    )

Best Practices​

🛠️ Tool Selection​

  • Use server-side tools for production applications requiring reliability and security
  • Use client-side tools for development, prototyping, or specialized integrations
  • Combine multiple tool types for comprehensive functionality

📝 Documentation​

  • Write clear, detailed docstrings for custom tools
  • Include parameter descriptions and expected return types
  • Test tool descriptions with the model to ensure proper usage

🔐 Security​

  • Store API keys securely using environment variables or secure configuration
  • Use the X-LlamaStack-Provider-Data header for dynamic authentication
  • Validate tool inputs and outputs for security

🔄 Error Handling​

  • Implement proper error handling in custom tools
  • Use structured error responses with meaningful messages
  • Monitor tool performance and reliability