Alat

Lulus tools saat membuat, memperbarui, atau memanggil waktu:

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."}]}],
)

Saat membuat harness baru secara interaktif, agentcore add harness wizard memungkinkan Anda memilih alat. Untuk menambahkan alat melalui CLI, gunakan agentcore add tool setelah membuat harness:

# 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"]}'

Menyebarkan untuk menerapkan.

Ganti alat pada satu pemanggilan:

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

Jalankan agentcore di direktori proyek, pilih tambahkan, pilih Harness, dan lanjutkan ke Pengaturan lanjutan. Aktifkan Tools with Space, lalu tekan Enter.

Kemudian jalankan agentcore deploy untuk melamar.

Lampirkan gateway pada waktu pembuatan (atau tools teruskanupdate_harness/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,
)

Alat gateway sekarang tersedia untuk agen. Panggil harness dengan prompt yang membutuhkan informasi terkini:

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."

Lewati alat fungsi inline pada waktu pemanggilan:

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

Agen melanjutkan penalaran dengan hasil alat dan mengalirkan respons akhir.

Tambahkan alat fungsi inline ke harness:

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

Kemudian tentukan deskripsi dan skema masukan diapp/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"]
      }
    }
  }
}

Jalankan agentcore deploy untuk melamar. Ketika agen memanggil fungsi inline selama pemanggilan, TUI berhenti dan meminta Anda untuk memberikan hasil alat sebaris. Dalam mode non-interaktif (CLI), aliran kembali stopReason: "tool_use" dengan dan Anda mengirim hasilnya kembali dengan panggilan panggilan tindak lanjut.