Tools

toolsBeim Erstellen, Aktualisieren oder Aufrufen übergeben:

tools = [
    # MCP server
    {
        "type": "remote_mcp",
        "name": "exa",
        "config": {"remoteMcp": {"url": "https://mcp.exa.ai/mcp"}},
    },
    # MCP server with authentication headers (plain text)
    {
        "type": "remote_mcp",
        "name": "my-private-mcp",
        "config": {"remoteMcp": {
            "url": "https://mcp.example.com/api",
            "headers": {"Authorization": "Bearer <your-token>"}
        }},
    },
    # MCP server with API key stored in AgentCore Identity Token Vault.
    # Use ${arn:...} to reference a credential provider - the ARN is resolved
    # to the actual API key at invocation time.
    {
        "type": "remote_mcp",
        "name": "exa-secure",
        "config": {"remoteMcp": {
            "url": "https://mcp.exa.ai/mcp",
            "headers": {"x-api-key": "${arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-exa-key}"}
        }},
    },
    # For managed credential rotation and OAuth-protected tools, put your MCP server
    # behind AgentCore Gateway and use AgentCore Identity instead of raw headers.
    #
    # AgentCore Gateway with SigV4 auth (default)
    {
        "type": "agentcore_gateway",
        "name": "my-gateway",
        "config": {"agentCoreGateway": {"gatewayArn": "arn:aws:bedrock-agentcore:us-west-2:123456789012:gateway/my-gateway"}},
    },
    # AgentCore Gateway with OAuth auth
    {
        "type": "agentcore_gateway",
        "name": "my-oauth-gateway",
        "config": {"agentCoreGateway": {
            "gatewayArn": "arn:aws:bedrock-agentcore:us-west-2:123456789012:gateway/my-oauth-gateway",
            "outboundAuth": {"oauth": {
                "credentialProviderName": "my-oauth-provider",
                "scopes": ["read", "write"]
            }}
        }},
    },
    # AgentCore Browser
    {"type": "agentcore_browser", "name": "browser"},
    # AgentCore Code Interpreter
    {"type": "agentcore_code_interpreter", "name": "code_interpreter"},
    # Inline function - executes on the client side, not on the harness VM.
    # When the agent calls this tool, the call is returned to your code for handling.
    {
        "type": "inline_function",
        "name": "approve_purchase",
        "config": {
            "inlineFunction": {
                "description": "Request human approval for a purchase.",
                "inputSchema": {
                    "type": "object",
                    "properties": {
                        "item": {"type": "string"},
                        "amount": {"type": "number"},
                    },
                    "required": ["item", "amount"],
                },
            }
        },
    },
]

response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    tools=tools,
    messages=[{"role": "user", "content": [{"text": "Find a mechanical keyboard under $200 and request approval."}]}],
)

Wenn Sie interaktiv einen neuen Kabelbaum erstellen, können Sie mit dem agentcore add harness Assistenten Werkzeuge auswählen. Um Tools über die CLI hinzuzufügen, verwenden Sie agentcore add tool nach dem Erstellen des Kabelbaums:

# Add a remote MCP server
agentcore add tool --harness my-agent --type remote_mcp \
  --name exa --url https://mcp.exa.ai/mcp

# Add a remote MCP server with an API key from AgentCore Identity Token Vault.
# Use ${arn:...} syntax in header values to reference a credential provider.
agentcore add tool --harness my-agent --type remote_mcp \
  --name exa-secure --url https://mcp.exa.ai/mcp \
  --header 'x-api-key=${arn:aws:bedrock-agentcore:us-west-2:123456789012:token-vault/default/apikeycredentialprovider/my-exa-key}'

# Add Browser
agentcore add tool --harness my-agent --type agentcore_browser --name browser

# Add Code Interpreter
agentcore add tool --harness my-agent --type agentcore_code_interpreter --name code-interpreter

# Add Gateway by ARN
agentcore add tool --harness my-agent --type agentcore_gateway \
  --name my-gateway --gateway-arn arn:aws:bedrock-agentcore:us-west-2:123456789012:gateway/my-gateway

# Add Gateway by project-local name
agentcore add tool --harness my-agent --type agentcore_gateway \
  --name my-gateway --gateway my-gateway

# Add an inline function tool (executes client-side, not on the harness VM)
agentcore add tool --harness my-agent --type inline_function \
  --name approve_purchase \
  --description "Request human approval for a purchase" \
  --input-schema '{"type": "object", "properties": {"item": {"type": "string"}, "amount": {"type": "number"}}, "required": ["item", "amount"]}'

Bereitstellen, um sich zu bewerben.

Tools bei einem einzigen Aufruf außer Kraft setzen:

agentcore invoke --harness research-agent --tools agentcore-browser "Find the latest news on AI agents"

Führen Sie das Programm agentcore in einem Projektverzeichnis aus, wählen Sie Hinzufügen, wählen Sie Harness und wechseln Sie zu den erweiterten Einstellungen. Aktivieren Sie Tools with Space und drücken Sie dann die Eingabetaste.

Starten Sie dannagentcore deploy, um sich zu bewerben.

Hängen Sie das Gateway bei der Erstellung an (oder geben Sieupdate_harness/toolsweiterinvoke_harness):

tools = [
    {
        "type": "agentcore_gateway",
        "name": "web-search",
        "config": {"agentCoreGateway": {
            "gatewayArn": "arn:aws:bedrock-agentcore:us-east-1:123456789012:gateway/my-web-search-gateway",
            "outboundAuth": {"awsIam": {}}
        }},
    },
]

client.create_harness(
    harnessName="research-agent",
    executionRoleArn="arn:aws:iam::123456789012:role/MyHarnessRole",
    tools=tools,
)

Die Tools des Gateways stehen dem Agenten jetzt zur Verfügung. Rufen Sie den Harness mit einer Aufforderung auf, für die aktuelle Informationen erforderlich sind:

response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    messages=[{"role": "user", "content": [{"text": "Search the web for the latest AWS announcements and cite your sources."}]}],
)

# Attach the web-search gateway to your harness
agentcore add tool --harness research-agent --type agentcore_gateway \
  --name web-search \
  --gateway-arn arn:aws:bedrock-agentcore:us-east-1:123456789012:gateway/my-web-search-gateway

# Deploy, then invoke
agentcore deploy
agentcore invoke --harness research-agent "Search the web for the latest AWS announcements and cite your sources."

Übergeben Sie beim Aufrufen ein Inline-Funktionstool:

# 1. Invoke with an inline function tool
response = client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    tools=[{
        "type": "inline_function",
        "name": "get_weather",
        "config": {"inlineFunction": {
            "description": "Get the current weather for a city.",
            "inputSchema": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"]
            }
        }}
    }],
    messages=[{"role": "user", "content": [{"text": "What's the weather in Seattle?"}]}],
)

# 2. The agent calls the tool - capture the toolUseId and input from the stream
tool_use_id = None
tool_name = None
tool_input = None
for event in response["stream"]:
    if "contentBlockStart" in event:
        start = event["contentBlockStart"].get("start", {})
        if "toolUse" in start and start["toolUse"].get("name") == "get_weather":
            tool_use_id = start["toolUse"]["toolUseId"]
            tool_name = start["toolUse"]["name"]
    if "contentBlockDelta" in event:
        delta = event["contentBlockDelta"].get("delta", {})
        if "toolUse" in delta:
            tool_input = (tool_input or "") + delta["toolUse"].get("input", "")

# 3. Execute the tool yourself and send the result back
# Include the assistant's toolUse message followed by your toolResult
client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    messages=[
        {
            "role": "assistant",
            "content": [{"toolUse": {"toolUseId": tool_use_id, "name": tool_name, "input": json.loads(tool_input)}}],
        },
        {
            "role": "user",
            "content": [{
                "toolResult": {
                    "toolUseId": tool_use_id,
                    "content": [{"text": "72°F, partly cloudy"}],
                    "status": "success",
                }
            }],
        },
    ],
)

Der Agent setzt die Argumentation anhand des Tool-Ergebnisses fort und streamt die endgültige Antwort.

Fügen Sie einem Kabelbaum ein integriertes Funktionswerkzeug hinzu:

agentcore add tool --harness my-agent --type inline_function \
  --name get_weather

Definieren Sie dann die Beschreibung und das Eingabeschema inapp/my-agent/harness.json:

{
  "type": "inline_function",
  "name": "get_weather",
  "config": {
    "inlineFunction": {
      "description": "Get the current weather for a city.",
      "inputSchema": {
        "type": "object",
        "properties": { "city": { "type": "string" } },
        "required": ["city"]
      }
    }
  }
}

Ausführenagentcore deploy, um sich zu bewerben. Wenn der Agent die Inline-Funktion während eines Aufrufs aufruft, hält die TUI an und fordert Sie auf, das Tool-Ergebnis inline bereitzustellen. Im nicht interaktiven Modus (CLI) kehrt der Stream mit zurück stopReason: "tool_use" und Sie senden das Ergebnis mit einem nachfolgenden Aufrufaufruf zurück.