Claude Architect · Lesson

Input Formats & Examples

Show concrete input examples to remove ambiguity.

Input Formats & Examples is a free Claude Architect lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Claude Architect learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Why Input Formats Matter

A tool description is the primary mechanism Claude uses to decide when and how to call a tool. Names alone don't carry enough signal. A strong description covers purpose, return values, edge cases, applicability boundaries, and crucially the input formats.

This lesson focuses on one high-leverage technique: showing concrete input examples so the model never has to guess what a parameter should look like.

Ambiguity Is the Enemy

Imagine a tool with a parameter called date. Is it 2026-06-10? 06/10/2026? June 10? A Unix timestamp? Without an example, Claude has to infer the format, and inference under ambiguity is where malformed tool calls come from.

An ambiguous schema doesn't fail loudly. It fails quietly, by producing inputs your backend rejects. Concrete examples remove that ambiguity at the source.

Where Examples Live

You can place input examples in two complementary places:

In the tool description text (overall usage examples).
In each parameter's description inside the JSON Schema (per-field format).

Both feed the same selection-and-formatting engine. Put the format guidance closest to the field it governs, and add a holistic example for the whole call.

search_orders = {
    "name": "search_orders",
    "description": (
        "Search a customer's orders by date range. "
        "Dates use ISO 8601 (YYYY-MM-DD). "
        "Example call: search_orders(start='2026-01-01', end='2026-03-31')."
    ),
    "input_schema": {
        "type": "object",
        "properties": {
            "start": {
                "type": "string",
                "description": "Inclusive start date, ISO 8601. Example: '2026-01-01'."
            },
            "end": {
                "type": "string",
                "description": "Inclusive end date, ISO 8601. Example: '2026-03-31'."
            }
        },
        "required": ["start", "end"]
    }
}

A Weak Description vs a Strong One

Compare these. The weak version forces guessing; the strong one shows exactly what valid input looks like.

Weak: "Look up a customer."
Strong: purpose + input format + example + return values + edge cases.

Minimal, ambiguous descriptions are a classic anti-pattern that causes tool misrouting and malformed arguments.

# Weak: model must guess the id format
bad = {
    "name": "get_customer",
    "description": "Look up a customer."
}

# Strong: shows the exact format with an example
good = {
    "name": "get_customer",
    "description": (
        "Fetch a verified customer profile by account ID. "
        "account_id is the 8-char alphanumeric code from the "
        "welcome email, e.g. 'A1B2C3D4' (not the email address). "
        "Returns name, tier, and verified flag. "
        "Returns isError if no match — ask for more identifiers, never guess."
    )
}

Show the Shape of Structured Inputs

When a parameter is an object or array, a single example is worth a paragraph of prose. Show the model the literal shape it should emit.

This is especially valuable for nested filters, list items, or any field where the structure isn't obvious from the type alone.

filter_param = {
    "type": "object",
    "description": (
        "Structured filter. Example: "
        '{"status": "shipped", "min_total": 50, '
        '"tags": ["priority", "gift"]}. '
        "Omit a key to leave that dimension unfiltered."
    ),
    "properties": {
        "status": {"type": "string", "enum": ["pending", "shipped", "delivered"]},
        "min_total": {"type": "number"},
        "tags": {"type": "array", "items": {"type": "string"}}
    }
}

Enums Beat Free Text for Fixed Sets

When a field has a known, finite set of valid values, encode them as an enum rather than describing them in prose. The schema then constrains the model directly.

For extensibility, add an "other" enum value plus a free-text detail field, so new cases don't force the model to invent an invalid value.

reason = {
    "type": "object",
    "properties": {
        "category": {
            "type": "string",
            "enum": ["defective", "wrong_item", "late", "other"],
            "description": "Refund reason. Use 'other' for anything unlisted."
        },
        "detail": {
            "type": "string",
            "description": "Free text. Required only when category is 'other'."
        }
    },
    "required": ["category"]
}

Examples Reduce Hallucinated Inputs

Few-shot examples are one of the most reliable prompting tools. With 2-4 targeted examples per ambiguity, the model generalizes the pattern rather than just repeating it.

Applied to tool inputs, examples are best for consistency, edge cases, output format, and reducing hallucination, exactly the failure modes that produce bad tool arguments.

phone = {
    "type": "string",
    "description": (
        "Phone in E.164 format. "
        "Examples: '+14155552671', '+442071838750'. "
        "Do NOT include spaces, dashes, or parentheses."
    )
}

Mark Required Only What's Always Present

Examples tell the model what valid input looks like; the required array tells it what must appear. A critical rule: mark a field required only if it is always present.

If you require a field that may be absent from the source, the model will fabricate a value to satisfy the schema. Optional-but-well-exemplified beats required-but-sometimes-missing.

schema = {
    "type": "object",
    "properties": {
        "order_id": {"type": "string", "description": "e.g. 'ORD-90412'"},
        "coupon_code": {
            "type": "string",
            "description": "Optional. e.g. 'SAVE10'. Omit if none on the order."
        }
    },
    # coupon_code is NOT required — it may be absent.
    "required": ["order_id"]
}

Call Out Edge Cases in the Example

Good descriptions state applicability boundaries: what the tool does, and what it does NOT handle. Bake those boundaries into your examples so the model recognizes when an input is out of scope.

This keeps overlapping tools from being misrouted, since the example clarifies which tool owns which input shape.

lookup_order = {
    "name": "lookup_order",
    "description": (
        "Look up ONE order by its order ID. "
        "order_id format: 'ORD-' + 5 digits, e.g. 'ORD-90412'. "
        "Does NOT search by customer name or email — "
        "use search_orders for that. "
        "Returns isError (category='validation') if the ID is malformed."
    )
}

Pair Input Examples With Structured Errors

Even with great examples, some inputs will be invalid. The tool's error contract should be just as explicit, so the model can recover.

Return structured errors: an isError flag plus errorCategory (transient / validation / business / permission), isRetryable, a message, and the attempted_query. Generic "Operation failed" blocks recovery; structured errors enable intelligent routing and a corrected retry.

{
  "isError": true,
  "errorCategory": "validation",
  "isRetryable": true,
  "message": "start must be ISO 8601 (YYYY-MM-DD); got '06/10/2026'.",
  "attempted_query": {"start": "06/10/2026", "end": "2026-03-31"},
  "partial_results": null
}

Examples + Retry-With-Feedback

When a tool input comes back malformed, use retry-with-feedback: send the original request, the wrong output, and the exact validation error back to the model. Format and structural errors are exactly what this fixes.

Note the limit: retry helps when the input was misformatted, not when the needed info is simply absent from the source. Examples prevent the first class of error; nothing invents missing facts.

messages.append({
    "role": "user",
    "content": (
        "Your tool call failed validation. "
        "start must match YYYY-MM-DD. "
        "You sent '06/10/2026'. "
        "Reissue the call with the corrected format."
    )
})
# Resend full history; the model keeps no state between turns.

Quick Check

Apply the lesson to a real design decision.

Recap: Make Inputs Unambiguous

Key takeaways:

Tool descriptions drive selection and formatting, so invest in them, not just names.
Show concrete input examples at the field level and a full-call example in the description.
Use enums (with an 'other' + detail field) for fixed sets; show the literal shape of objects and arrays.
Mark a field required only if it is always present, otherwise the model fabricates.
Back examples with structured errors (errorCategory, isRetryable, attempted_query) so retry-with-feedback can correct format mistakes, though it cannot supply absent facts.

Concrete examples are the cheapest, highest-leverage way to stop malformed tool calls before they happen.

Frequently asked questions

Is the “Input Formats & Examples” lesson free?

Yes — the full text of “Input Formats & Examples” is free to read here on the web, and the Claude Architect course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Claude Architect course, upgrade to CoddyKit PRO.

What will I learn in “Input Formats & Examples”?

Show concrete input examples to remove ambiguity. You practise Claude Architect with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Claude Architect?

No prior experience is required. Claude Architect on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Input Formats & Examples” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Claude Architect lesson?

Yes. Every Claude Architect lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

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