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Agent Invents API Parameters That Don’t Exist — API Call Fails with Unknown Field

Symptom

  • API returns 400 Bad Request: Unknown field 'max_length'
  • Agent uses model.generate(temperature=0.9) but the method doesn’t have that parameter
  • Agent calls Anthropic API with top_k=40 on a model that doesn’t support it
  • API silently ignores unknown parameters — call succeeds but behavior is unexpected
  • Agent mixes up parameter names from different SDKs (e.g., OpenAI vs Anthropic)

Root Cause

The model’s training data contains API documentation from many versions and providers. Parameters from one API leak into calls for another (e.g., OpenAI’s max_tokens vs Anthropic’s max_tokens, or confusing temperature ranges). When the agent doesn’t have the current API schema in context, it confabulates plausible-sounding parameters.

Fix

Option 1: Include the exact API signature in the system prompt

System prompt:
"You are calling the Anthropic Messages API. The EXACT valid parameters are:

client.messages.create(
    model: str,          # Required: e.g. 'claude-sonnet-4-6'
    max_tokens: int,     # Required: maximum output tokens
    messages: list,      # Required: list of {role, content} dicts
    system: str,         # Optional: system prompt
    temperature: float,  # Optional: 0.0-1.0
    top_p: float,        # Optional: 0.0-1.0
    top_k: int,          # Optional: integer
    stream: bool,        # Optional: enable streaming
    stop_sequences: list, # Optional: list of stop strings
    tools: list,         # Optional: tool definitions
    tool_choice: dict,   # Optional: tool selection mode
)

DO NOT use any parameter not listed here. Parameters not listed do not exist."

Option 2: Validate parameters against schema before calling

from typing import Any

ANTHROPIC_MESSAGES_SCHEMA = {
    "required": {"model", "max_tokens", "messages"},
    "optional": {
        "system", "temperature", "top_p", "top_k",
        "stream", "stop_sequences", "tools", "tool_choice",
        "metadata"
    }
}

def validate_api_call(params: dict, schema: dict) -> list[str]:
    """Check for unknown parameters before making API call"""
    allowed = schema["required"] | schema["optional"]
    unknown = set(params.keys()) - allowed
    missing = schema["required"] - set(params.keys())

    errors = []
    if unknown:
        errors.append(f"Unknown parameters: {unknown}. Allowed: {allowed}")
    if missing:
        errors.append(f"Missing required parameters: {missing}")
    return errors

# Before calling API
params = {
    "model": "claude-sonnet-4-6",
    "max_tokens": 1024,
    "messages": [...],
    "max_length": 100,  # hallucinated parameter!
}

errors = validate_api_call(params, ANTHROPIC_MESSAGES_SCHEMA)
if errors:
    raise ValueError(f"Invalid API parameters: {errors}")

Option 3: Use typed dataclasses / pydantic models for API calls

from pydantic import BaseModel, Field, model_validator
from typing import Optional

class AnthropicMessageRequest(BaseModel):
    model: str
    max_tokens: int
    messages: list[dict]
    system: Optional[str] = None
    temperature: Optional[float] = Field(None, ge=0.0, le=1.0)
    top_p: Optional[float] = Field(None, ge=0.0, le=1.0)
    top_k: Optional[int] = Field(None, ge=1)
    stream: Optional[bool] = None
    stop_sequences: Optional[list[str]] = None

    class Config:
        extra = "forbid"  # Raise error on unknown fields

# Agent generates params as dict — validate before calling
try:
    request = AnthropicMessageRequest(**agent_generated_params)
    response = client.messages.create(**request.model_dump(exclude_none=True))
except ValidationError as e:
    print(f"Agent hallucinated invalid params: {e}")
    # Re-prompt agent with correct schema

Option 4: Fetch live API schema from documentation

# For OpenAPI-compatible APIs, fetch the actual schema
import httpx, yaml

def get_api_schema(openapi_url: str) -> dict:
    """Fetch live API schema to ground agent in current spec"""
    response = httpx.get(openapi_url)
    return yaml.safe_load(response.text)

def extract_endpoint_params(schema: dict, endpoint: str, method: str = "post") -> list[str]:
    """Extract valid parameter names for a specific endpoint"""
    path = schema.get("paths", {}).get(endpoint, {})
    operation = path.get(method, {})
    params = []
    for param in operation.get("parameters", []):
        params.append(param["name"])
    body_schema = operation.get("requestBody", {}).get("content", {}).get("application/json", {}).get("schema", {})
    params.extend(body_schema.get("properties", {}).keys())
    return params

Option 5: Parameter cross-reference table in prompt

System prompt:
"IMPORTANT — API parameter name differences:

| Concept | OpenAI | Anthropic |
|---------|--------|-----------|
| Max output tokens | max_tokens | max_tokens |
| Sampling temperature | temperature | temperature |
| Nucleus sampling | top_p | top_p |
| Model name | model | model |
| System prompt | messages[system] | system (top-level) |
| Stop words | stop | stop_sequences |
| Streaming | stream | stream |
| N completions | n | NOT SUPPORTED |
| Presence penalty | presence_penalty | NOT SUPPORTED |
| Frequency penalty | frequency_penalty | NOT SUPPORTED |
| Logprobs | logprobs | NOT SUPPORTED |

Never use OpenAI-only parameters with the Anthropic API."

Common Hallucinated Parameters for Anthropic API

Hallucinated param Actually exists? What to use instead
max_length No max_tokens
presence_penalty No Not available
frequency_penalty No Not available
n No Make N separate calls
logprobs No Not available
best_of No Not available
response_format No (as of 2025) Prompt for JSON
functions No tools

Expected Token Savings

Debugging hallucinated parameter errors: ~3,000 tokens Schema in system prompt prevents all: 0 wasted

Environment

  • Any agent making API calls, especially when switching between providers
  • Source: direct experience; extremely common when agents use multiple API providers

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