Agent.Tools

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Tool Methods

Methods for discovering, executing, and managing tools in the xpander SDK.

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get_tools() / getTools()

Returns available tools, optionally formatted for a specific LLM provider.

tools = agent.get_tools()

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run_tool() / runTool()

Executes a single tool by name with specified input parameters.

result = agent.run_tool(
    tool_name="web_search",
    tool_input={"query": "latest AI advancements"}
)

Returns: ToolCallResult object containing the execution result.

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run_tools() / runTools()

Executes multiple tool calls extracted from an LLM response.

results = agent.run_tools(tool_calls=tool_calls)

Returns: Array of ToolCallResult objects containing execution results.

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add_tool_call_results() / addToolCallResults()

Adds tool execution results to the agent’s message history as tool messages.

from xpander_sdk import ToolCallResult

tool_results = [
    ToolCallResult(
        function_name="get_weather",
        tool_call_id="call-123",
        is_success=True,
        result="The weather in New York is sunny with a temperature of 72°F.",
        payload={"location": "New York"}
    )
]

agent.add_tool_call_results(tool_call_results=tool_results)

Returns: void

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add_local_tools() / addLocalTools()

Adds custom, locally-implemented tools to the agent.

agent.add_local_tools([{
    "name": "tool_name",
    "description": "Tool description",
    "function": function_reference,
    "parameters": {
        "type": "object",
        "properties": {
            "param1": {
                "type": "string",
                "description": "Parameter description"
            }
        },
        "required": ["param1"]
    }
}])

Returns: void

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Tool Definition Object Properties

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Static Utility Methods

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XpanderClient.format_tools() / formatTools()

Static utility method to format tools for various LLM providers.

from xpander_sdk import XpanderClient, LLMProvider

tools_formatted = XpanderClient.format_tools(
    tools=[
        {
            "name": "search",
            "description": "Search for information",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query"
                    }
                },
                "required": ["query"]
            }
        }
    ],
)

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Parameters

Returns tools formatted for the specified LLM provider.

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agent.extract_tool_calls() / extractToolCalls()

Extracts tool calls from an LLM response using the agent’s selected provider.

response = openai_client.chat.completions.create(
    model="gpt-4.1",
    messages=agent.messages,
)
tool_calls = agent.extract_tool_calls(llm_response=response.model_dump())

​

agent.retrieve_pending_local_tool_calls() / retrievePendingLocalToolCalls()

Filters tool calls to retrieve only those that should be processed locally.

local_tool_calls = agent.retrieve_pending_local_tool_calls(
    tool_calls=tool_calls
)

Returns: Array of ToolCall objects that should be processed locally.

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Agentic Interface Methods

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retrieve_agentic_interfaces() / retrieveAgenticInterfaces()

Returns available connector interfaces that provide pre-built operations.

interfaces = agent.retrieve_agentic_interfaces()

Returns: Array of AgenticInterface objects representing available connectors.

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retrieve_agentic_operations() / retrieveAgenticOperations()

Retrieves available operations from a specific interface.

operations = agent.retrieve_agentic_operations(agentic_interface=interface)

Returns: Array of Operation objects available from the specified interface.

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attach_operations() / attachOperations()

Attaches operations to the agent so they become available as tools.

agent.attach_operations(operations=[operation1, operation2])

Returns: void

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Type Definitions

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ToolCall

Represents a tool call extracted from an LLM response.

interface ToolCall {
  id: string;
  name: string;
  type: ToolCallType;
  payload: any;
  graphApproved: boolean;
}

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ToolCallResult

Represents the result of executing a tool call.

interface ToolCallResult {
  toolCallId: string;
  functionName: string;
  isSuccess: boolean;
  result: string;
  payload: any;
  graphApproved: boolean;
}

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AgenticInterface

Represents a connector interface that provides operations.

interface AgenticInterface {
  id: string;
  name: string;
  description: string;
  iconUrl: string;
}

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Operation

Represents a pre-built operation from an interface.

interface Operation {
  id: string;
  name: string;
  description: string;
  interface: AgenticInterface;
  parameters: object;
}

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Usage Example

# Extract tool calls and execute them
tool_calls = agent.extract_tool_calls(llm_response=response)
results = agent.run_tools(tool_calls=tool_calls)

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Related Documentation