supervisor-api-client-function-calling

name	supervisor-api-client-function-calling
description	Add client-side function tools to the Supervisor API. Use when: (1) User wants to mix Python callables with hosted tools, (2) User asks about function tools with Supervisor API, (3) User needs to execute custom business logic alongside hosted tool calls.

Client-Side Function Tools with the Supervisor API

Client-side function tools (type: "function") let you declare callables your application executes — business logic, calls to your own database, external APIs, etc. The Supervisor API returns the pending function_call, your app executes the function, and you resume the conversation by appending a function_call_output to the next request's input.

Tool declaration constraints:

• name must match ^[a-zA-Z0-9_-]{1,64}$ (no dots, spaces, or other characters).
• parameters must be a JSON Schema object, or omitted.

Continuation pattern: the Supervisor API uses full-history echo. On each subsequent request, append the prior response.output to your input list, then add function_call_output items for every client function_call you executed. (previous_response_id chaining is on the roadmap.)

Function-Only Flow

The simplest case: a single client-side function tool, no hosted tools.

import json
from databricks.sdk import WorkspaceClient
from databricks_openai import DatabricksOpenAI

client = DatabricksOpenAI(workspace_client=WorkspaceClient(), use_ai_gateway=True)
MODEL = "databricks-claude-sonnet-4-5"

GET_WEATHER = {
    "type": "function",
    "name": "get_weather",
    "description": "Get current weather for a location.",
    "parameters": {
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
        "additionalProperties": False,
    },
}

def run_get_weather(args):
    return json.dumps({
        "location": args["location"],
        "temp_c": 18,
        "condition": "sunny",
    })

CLIENT_TOOLS = {"get_weather": run_get_weather}
TOOLS = [GET_WEATHER]

input_list = [{"role": "user", "content": "What's the weather in Paris?"}]

# Turn 1 — model emits a function_call
resp = client.responses.create(model=MODEL, input=input_list, tools=TOOLS)

# Echo the model's turn into history, then execute pending client function_calls
input_list += [item.model_dump() for item in resp.output]
for item in resp.output:
    if item.type == "function_call" and item.name in CLIENT_TOOLS:
        args = json.loads(item.arguments)
        input_list.append({
            "type": "function_call_output",
            "call_id": item.call_id,
            "output": CLIENT_TOOLS[item.name](args),
        })

# Turn 2 — model produces the final assistant message using the tool result
final = client.responses.create(model=MODEL, input=input_list, tools=TOOLS)
print(final.output_text)

Streaming Function-Only Flow

The same pattern works with streaming. Collect the full response from the stream, execute pending function calls, then resume with a second streamed call.

import json
from databricks.sdk import WorkspaceClient
from databricks_openai import DatabricksOpenAI

client = DatabricksOpenAI(workspace_client=WorkspaceClient(), use_ai_gateway=True)
MODEL = "databricks-claude-opus-4-6"

GET_WEATHER = {
    "type": "function",
    "name": "get_weather",
    "description": "Get the current weather for a location.",
    "parameters": {
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
        "additionalProperties": False,
    },
}

def run_get_weather(args):
    return json.dumps({
        "location": args["location"],
        "temp_c": 18,
        "condition": "sunny",
    })

CLIENT_TOOLS = {"get_weather": run_get_weather}
TOOLS = [GET_WEATHER]

input_list = [{"role": "user", "content": "What's the weather in Paris?"}]

# ── First streamed call: collect the response containing function_call(s) ──
stream = client.responses.create(
    model=MODEL,
    tools=TOOLS,
    input=input_list,
    stream=True,
)

first_response = None
for event in stream:
     event. == :
        first_response = event.response


input_list += [item.model_dump()  item  first_response.output]
 item  first_response.output:
     item. ==   item.name  CLIENT_TOOLS:
        args = json.loads(item.arguments)
        input_list.append({
            : ,
            : item.call_id,
            : CLIENT_TOOLS[item.name](args),
        })


followup = client.responses.create(
    model=MODEL,
    tools=TOOLS,
    =input_list,
    stream=,
)

 event  followup:
     event. == :
        (event.response.output_text)

Mixed Flow: Hosted UC Function + Client-Side Function Tool

When you combine hosted tools (e.g. uc_function) with client-side function tools, the server executes hosted tool calls and returns their results in response.output alongside any pending client-side function_call items. Your code only needs to execute the client-side calls — skip hosted ones.

import json
from databricks.sdk import WorkspaceClient
from databricks_openai import DatabricksOpenAI

client = DatabricksOpenAI(workspace_client=WorkspaceClient(), use_ai_gateway=True)
MODEL = "databricks-claude-sonnet-4-5"

GET_WEATHER = {
    "type": "function",
    "name": "get_weather",
    "description": "Get the current weather for a location.",
    "parameters": {
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
        "additionalProperties": False,
    },
}

def run_get_weather(args):
    return json.dumps({
        "location": args["location"],
        "temp_c": 18,
        "condition": "sunny",
    })

CLIENT_TOOLS = {"get_weather": run_get_weather}

uc_function = {"type": "uc_function", "uc_function": {"name": "system.ai.python_exec"}}

TOOLS = [GET_WEATHER, uc_function]

input_list = [
    {
        "role": "user",
        "content": (
            "Run python_exec to get 15th fibonacci number "
            "AND call get_weather for Paris. Use both tools."
        ),
    }
]


first = client.responses.create(
    model=MODEL,
    tools=TOOLS,
    =input_list,
)




input_list += [item.model_dump()  item  first.output]
 item  first.output:
     item. != :
        
    impl = CLIENT_TOOLS.get(item.name)
     impl  :        
        
    args = json.loads(item.arguments)
    input_list.append({
        : ,
        : item.call_id,
        : impl(args),
    })


final = client.responses.create(
    model=MODEL,
    tools=TOOLS,
    =input_list,
)
(final.output_text)

Mixed Flow: MCP Approval + Client-Side Function Tools

When you mix MCP-approval-gated hosted tools with client-side function tools, both kinds need handling each turn. The supervisor serializes turns (one tool at a time per response), so a given response will have at most one kind pending — but the client loop has to cover both.

import json
from databricks.sdk import WorkspaceClient
from databricks_openai import DatabricksOpenAI
from openai.types.responses.response_output_item import McpApprovalResponse

client = DatabricksOpenAI(workspace_client=WorkspaceClient(), use_ai_gateway=True)
MODEL = "databricks-claude-sonnet-4-5"

# Hosted MCP server (require_approval flow) + client-side function tool
TOOLS = [
    {
        "type": "uc_connection",
        "uc_connection": {
            "name": "<uc-connection-name>",
            "description": "Searches the web for current information",
        },
    },
    {
        "type": "function",
        "name": "get_weather",
        "description": "Get current weather for a location.",
        "parameters": {
            "type": "object",
            "properties": {"location": {"type": "string"}},
            "required": ["location"],
            "additionalProperties": False,
        },
    },
]

def run_get_weather(args):
    return json.dumps({
        "location": args["location"],
        "temp_c": 18,
        "condition": "sunny",
    })

CLIENT_TOOLS = {: run_get_weather}


 ():
    
     (
        o. == 
         (o. ==   (o, , )  client_tools)
         o  resp.output
    )


 ():
    
    next_input = (input_list) + [item.model_dump()  item  prev_resp.output]
     item  prev_resp.output:
         item. == :
            approval = McpApprovalResponse(
                =item.,
                approval_request_id=item.,
                approve=,
                =,
            )
            next_input.append(approval.to_dict())
         item. == :
            impl = client_tools.get((item, , ))
             impl  :
                
                
            args = json.loads(item.arguments)
            next_input.append({
                : ,
                : item.call_id,
                : impl(args),
            })
     next_input


input_list = [
    {
        : ,
        : ,
        : (
            
            
        ),
    }
]

resp = client.responses.create(model=MODEL, =input_list, tools=TOOLS)
 has_pending(resp, CLIENT_TOOLS):
    input_list = execute_pending(resp, input_list, CLIENT_TOOLS)
    resp = client.responses.create(model=MODEL, =input_list, tools=TOOLS)

(resp.output_text)

What's happening per turn (typical):

• Turn 1 — model picks the search tool first → output: [..., mcp_approval_request]
• Turn 2 (after approval) — server executes MCP, returns its function_call_output, model decides to call get_weather → output: [..., function_call_output, function_call]
• Turn 3 (after client executes get_weather) — model has both tool results and writes the final answer → output: [message]

The exact number of turns depends on the prompt and on retry behavior (e.g., the MCP server may reject the model's first-attempt arguments and the model self-corrects on the next turn). The drain loop handles all cases — it keeps iterating until response.output has no pending items.

Why client tools must be filtered by name: hosted tools (UC functions, executed MCP calls) also emit items with type: "function_call" in response.output. These represent work Databricks already executed; you must NOT execute them locally. Filter by client_tools.get(item.name) and skip when None — the hosted function_call is already paired with its function_call_output in the same response and just needs to be echoed back as conversation history.

Troubleshooting

"function tool 'name' must match ^[a-zA-Z0-9_-]{1,64}$" — The name field on a type: "function" tool contains invalid characters (dots, spaces, etc.) or is longer than 64 chars. Rename the tool.

"No tool output found for function call ." — A prior client-side function_call in the input list has no matching function_call_output. Append the output before resuming.

"No tool call found for function call output with call_id ." — A function_call_output in input references a call_id no prior item introduced. Echo the originating function_call (or mcp_approval_request) in the input list.