Connect to tools

When creating a new harness interactively, the agentcore add harness wizard lets you select tools. To add tools via the CLI, use agentcore add tool after creating the harness:

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

# 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"]}'

Deploy to apply.

Override tools on a single invocation:

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

Pass tools at create, update, or invoke time:

tools = [
    # MCP server
    {
        "type": "remote_mcp",
        "name": "exa",
        "config": {"remoteMcp": {"url": "https://mcp.exa.ai/mcp"}},
    },
    # MCP server with authentication headers
    {
        "type": "remote_mcp",
        "name": "my-private-mcp",
        "config": {"remoteMcp": {
            "url": "https://mcp.example.com/api",
            "headers": {"Authorization": "Bearer <your-token>"}
        }},
    },
    # For managed credential rotation and API key storage, 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."}]}],
)

Add an inline function tool to a harness:

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

Then define the description and input schema in 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"]
      }
    }
  }
}

Run agentcore deploy to apply. When the agent calls the inline function during an invocation, the TUI pauses and prompts you to provide the tool result inline. In non-interactive (CLI) mode, the stream returns with stopReason: "tool_use" and you send the result back with a follow-up invoke call.

Pass an inline function tool at invoke time:

# 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 from the stream
tool_use_id = None
for event in response["stream"]:
    if "contentBlockStart" in event:
        start = event["contentBlockStart"].get("start", {})
        if "toolUse" in start and start["toolUse"].get("type") == "inline_function":
            tool_use_id = start["toolUse"]["toolUseId"]

# 3. Execute the tool yourself and send the result back
client.invoke_harness(
    harnessArn=HARNESS_ARN,
    runtimeSessionId=SESSION_ID,
    messages=[{
        "role": "user",
        "content": [{
            "toolResult": {
                "toolUseId": tool_use_id,
                "content": [{"text": "72°F, partly cloudy"}],
                "status": "success",
            }
        }],
    }],
)

The agent resumes reasoning with the tool result and streams the final response.