rohitg00/ai-engineering-from-scratch · error · ValueError

review_goal.content must be an object

Error message

review_goal.content must be an object

What it means

Inside the review_goal elicitation response, content must be an object because it carries the form fields — here the 'goal' string. A string or list content, even a plausible one like the goal text itself, is rejected before goal extraction.

Source

Thrown at certifications/claude/lessons/11-mcp-server-design-and-integration/code/main.py:415

        missing_responses = {
            key: request for key, request in input_requests.items() if key not in responses
        }
        if missing_responses:
            return self._input_required(
                inputRequests=missing_responses, requestState=state
            )

        workspace_scope = responses["workspace_scope"]
        review_sample = responses["review_sample"]
        elicitation = responses["review_goal"]
        if not all(
            isinstance(response, dict)
            for response in (workspace_scope, review_sample, elicitation)
        ):
            raise ValueError("inputResponses entries must be objects")
        elicitation_content = elicitation.get("content", {})
        if not isinstance(elicitation_content, dict):
            raise ValueError("review_goal.content must be an object")
        roots = workspace_scope.get("roots")
        sample_content = review_sample.get("content", {})
        goal = elicitation_content.get("goal")
        if not isinstance(roots, list) or not isinstance(sample_content, dict):
            raise ValueError("inputResponses contain invalid roots or sampling results")
        if elicitation.get("action") != "accept" or not isinstance(goal, str):
            raise ValueError("review_goal must be accepted with a string goal")
        sample = sample_content.get("text")
        if not isinstance(sample, str):
            raise ValueError("review_sample must contain text")
        summary = {
            "goal": goal,
            "rootCount": len(roots),
            "sample": sample,
            "topic": arguments["topic"],
        }
        return self._complete(
            content=[{"type": "text", "text": json.dumps(summary, sort_keys=True)}],

View on GitHub (pinned to 39ea8a1c6d)

Solutions

1. Match the requestedSchema in the server's elicitation/create params — content is an object with a goal string field
2. Wrap free-text goals: content = {"goal": value}"
3. Log the elicitation request's requestedSchema before building the response

Example fix

# before
review_goal = {"action": "accept", "content": "check error handling"}

# after
review_goal = {"action": "accept", "content": {"goal": "check error handling"}}

content = elicitation_response.get("content")
if not isinstance(content, dict):
    elicitation_response["content"] = content = {}
if not isinstance(content.get("goal"), str):
    raise ValueError("elicitation content must be an object with a string goal")

def is_form_content(content: object) -> bool:
    return isinstance(content, dict) and isinstance(content.get("goal"), str)

When it happens

Trigger: Sending {"action": "accept", "content": "review the auth flow"} instead of {"action": "accept", "content": {"goal": "review the auth flow"}}.

Common situations: Elicitation form output flattened to a plain string by middleware; a client that models elicitation content like sampling text content; schema drift between the client form and the server's requestedSchema.

Understand the failure class

Background: Schema validation failed / invalid input schema: payload rejected because its shape doesn't match the expected schema — this error's family across 28 libraries.

Related errors

• inputResponses entries must be objects
• review_goal must be accepted with a string goal
• -32600
• -32603
• expected object

AI-assisted analysis of rohitg00/ai-engineering-from-scratch@39ea8a1c6d (2026-08-26). Data as JSON: /api/errors/a691c97071e5e08d. Report an issue: GitHub.