入力形式と例
具体的な入力例を示して曖昧さをなくします
「入力形式と例」はCoddyKit上の無料Claude Architectレッスンです。 これはレッスン4/4です。 下記で完全なレッスンを無料で読むことができます。その後、ブラウザ内の組み込みコードエディタと24時間対応のAIチューターでハンズオン演習できます。 これはClaude Architect学習パスの一部であり、ウェブとCoddyKitアプリ全体で進捗が同期されます。 Claude Architectコースには全4レッスンが含まれています。
このレッスンの一部はまだ翻訳されておらず、英語で表示されています。
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
descriptioninside 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.
よくある質問
「入力形式と例」レッスンは無料ですか？
はい。「入力形式と例」の完全なテキストはこのウェブで無料で読めます。インタラクティブに演習し（組み込みコードエディタと24時間対応のAIチューター）、Claude Architectコースの残りをアンロックするには、CoddyKit PROにアップグレードしてください。 Claude Architectコースには全4レッスンが含まれています。
「入力形式と例」で何を学びますか？
具体的な入力例を示して曖昧さをなくします ブラウザで直接実行するハンズオンコードでClaude Architectを演習し、24時間対応のAIチューターがレッスンを進める中での質問に答えます。
Claude Architectを始めるのに経験は必要ですか？
事前経験は必要ありません。CoddyKitのClaude Architectは初級者から上級者向けに構成されているため、ここから始めるか最初から始めて、自分のペースで進むことができます。 これはレッスン4/4です。
「入力形式と例」レッスンにはどのくらい時間がかかりますか？
ほとんどのCoddyKitレッスンは約5～10分かかります。各レッスンはコンパクトでインタラクティブなので、着実に進歩し、ウェブとアプリ全体で正確に前回の場所から再開できます。
このClaude Architectレッスンでコードを書いて実行できますか？
はい。すべてのClaude Architectレッスンに組み込みコードエディタが含まれているため、ブラウザでリアルコードを書いて実行し、即座のAIフィードバックを取得できます。ローカル設定は不要です。