ツール

作成、更新、または呼び出しtools時に を渡します。

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."}]}],
)

新しいハーネスをインタラクティブに作成すると、agentcore add harnessウィザードでツールを選択できます。CLI 経由でツールを追加するには、ハーネスの作成agentcore add tool後に を使用します。

# 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"]}'

デプロイして適用します。

1 回の呼び出しでツールを上書きします。

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

プロジェクトディレクトリagentcoreで を実行し、 の追加 を選択し、ハーネス を選択し、詳細設定 に進みます。Tools with Space を有効にし、Enter キーを押します。

次に、 を実行して適用agentcore deployします。

作成時にゲートウェイをアタッチする (または update_harness / toolsを渡すinvoke_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,
)

ゲートウェイのツールがエージェントで使用できるようになりました。現在の情報を必要とするプロンプトでハーネスを呼び出します。

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."

呼び出し時にインライン関数ツールを渡します。

# 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",
                }
            }],
        },
    ],
)

エージェントはツールの結果で推論を再開し、最終レスポンスをストリーミングします。

インライン関数ツールをハーネスに追加します。

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

次に、 で説明と入力スキーマを定義しますapp/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"]
      }
    }
  }
}

を実行して適用agentcore deployします。エージェントが呼び出し中にインライン関数を呼び出すと、TUI は一時停止し、ツールの結果をインラインで提供するように求められます。非インタラクティブ (CLI) モードでは、ストリームは で戻りstopReason: "tool_use"、フォローアップ呼び出し呼び出しで結果を返します。