| name | json-mode-patterns |
| description | Structured JSON output from Claude: tool-use-as-JSON, schema, parsing, partial recovery. Triggers: JSON mode, structured output, schema validation, JSON parsing. |
| effort | medium |
| user-invocable | false |
| allowed-tools | Read |
JSON Mode Patterns
Claude does not have a dedicated response_format: json parameter like some other APIs. The idiomatic way to get guaranteed JSON is tool use with a forced function call. This skill documents that pattern plus fallbacks.
Preferred Pattern: Tool-as-Schema
Define a tool whose input schema IS the JSON shape you want, then force the model to call it.
tools = [{
"name": "record_analysis",
"description": "Return the analysis as structured data",
"input_schema": {
"type": "object",
"properties": {
"sentiment": {"type": "string", "enum": ["positive", "neutral", "negative"]},
"confidence": {"type": "number", "minimum": 0, "maximum": 1},
"themes": {"type": "array", "items": {"type": "string"}}
},
"required": ["sentiment", "confidence", "themes"]
}
}]
response = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
tools=tools,
tool_choice={"type": "tool", "name": "record_analysis"},
messages=[{"role": "user", "content": text_to_analyze}]
)
for block in response.content:
if block.type == "tool_use" and block.name == "record_analysis":
result = block.input
break
Why this wins:
- Schema is enforced at the API level
- No regex or parsing from model text
- Enums, min/max, required fields actually constrain the output
Fallback: Prompted JSON + Strict Parse
When tool use is unavailable (some SDKs/proxies strip it):
response = client.messages.create(
model="claude-opus-4-8",
max_tokens=1024,
system="You return ONLY valid JSON. No prose, no markdown fences.",
messages=[{
"role": "user",
"content": f"Extract as JSON matching this schema: {schema_str}\n\nInput: {text}"
}]
)
import json
try:
result = json.loads(response.content[0].text)
except json.JSONDecodeError:
result = json.loads(strip_markdown_fence(response.content[0].text))
Add a regex fallback that extracts the first {...} block if the model added a preface.
Schema Design Rules
- Favor enums over free-form strings when values are known
- Mark required fields aggressively — default-optional produces flaky output
- Use arrays of objects, not parallel arrays (
[{name, value}] over {names: [], values: []})
- Shallow beats nested — 2 levels of nesting max unless necessary
- Document each field in the tool's
description as well as the schema
Partial Output Recovery
Model hits max_tokens mid-JSON. Strategies:
- Increase
max_tokens if the schema is genuinely large (most common cause).
- Split the schema — generate one field per call, merge.
- Use streaming and close unclosed braces if stop reason is
max_tokens.
if response.stop_reason == "max_tokens":
raise IncompleteOutputError(...)
Validation After Parse
Even with tool schema enforcement, business rules aren't enforced by JSON Schema. Add a Pydantic/Zod layer:
from pydantic import BaseModel, Field
class Analysis(BaseModel):
sentiment: Literal["positive", "neutral", "negative"]
confidence: float = Field(ge=0, le=1)
themes: list[str] = Field(min_length=1, max_length=10)
parsed = Analysis(**result)
Gotchas
- Tool call tokens count toward output budget — a huge schema eats max_tokens fast
stop_reason == "tool_use" is success, not an error
- Streaming with tool use requires handling
content_block_delta events with input_json_delta deltas
- Model picks a different tool than you expected if
tool_choice is "auto" — always force the specific tool for JSON mode
Related