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Bug in the official tutorial docs: Tool use example code lacks agentic loop, causing multi-turn calls to fail

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Bewertung

Schwierigkeit
3/5
Geschätzter Aufwand
1-2 Tage
Anfängerfreundlichkeit
76/100
Issue-Typ
Bug
Klarheit
Klar beschrieben
Aktivitätsstatus
Aktiv
Tech-Stack
python
Bereich
documentation

Rechercherichtung

Start with “The Query Processing Logic” in the MCP building tutorial at the linked build-client page, focusing on the async process_query function and its message handling. Confirm that repeated tool_use responses are processed with their tool results until a plain-text response is returned, including tool execution errors; the issue’s corrected snippet defines the expected behavior.

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Beschreibung

The original example code (The Query Processing Logic in the MCP building tutorial site https://modelcontextprotocol.io/docs/2026-07-28/develop/build-client) has the following core issues:

  • Single-turn Limitation: After the initial API call, the code only checks and processes tool_use blocks once. If Claude requests additional tool calls in its second response (e.g., needing to chain a second tool based on the output of the first), the original code fails to handle them—it simply ignores subsequent tool calls, causing complex multi-step tasks to fail.
  • Lack of Agentic Loop Structure: It does not use a loop to continuously maintain conversation state. A correct implementation should, after each model response, check whether tool_use blocks are present; if so, execute the tools, append the results to the message history, and continue with the next API call—repeating this cycle until the model outputs a plain-text final answer.

The following corrected snippet resolves these issues by introducing a while True loop, correctly implementing a multi-turn tool-calling agent loop. Below is the corrected code version:

async def process_query(client, query: str) -> str:
    
    messages = [
        {"role": "user",
         "content": query}
    ]

    tool_list = await client.list_tools()
    available_tools = [{
        "name": tool.name,
        "description": tool.description,
        "input_schema": tool.input_schema,
    } for tool in tool_list.tools]

    final_texts = []

    while True:
        response = await anthropic.messages.create(
            model=MODEL,
            max_tokens=1000,
            messages=messages,
            tools=available_tools,
        )

        # Append the full assistant response back to the message history for context
        messages.append({
            "role": "assistant",
            "content": response.content,
        })

        tool_uses = []

        for block in response.content:
            if block.type == "text":
                final_texts.append(block.text)
            elif block.type == "tool_use":
                tool_uses.append(block)

        # If no tool_use is requested, this is the final answer; exit the loop
        if not tool_uses:
            break

        tool_results = []

        for block in tool_uses:
            try:
                result = await client.call_tool(block.name, block.input)

                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": format_tool_result(result),  # Assumes format_tool_result is defined elsewhere
                    "is_error": bool(result.is_error),
                })
            except Exception as e:
                # Tool execution errors must also be returned as tool_result to the model
                tool_results.append({
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": f"Tool execution error: {e}",
                    "is_error": True,
                })

        # Feed the tool results back as a user message to continue the next turn
        messages.append({
            "role": "user",
            "content": tool_results,
        })

    return "\n".join(final_texts)

(Note: format_tool_result is assumed to be a helper function that extracts text from the tool response, similar to "\n".join(block.text for block in result.content if isinstance(block, TextContent)) in the original code.)

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