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Author SHA1 Message Date
96f6b78a93 refactor: hide context parameters from AI model tool schema
Prevent AI hallucination of runtime parameters by hiding them from the tool schema.

Architecture:
- Public tool functions (buy/sell) only expose symbol and amount to AI
- Use **kwargs to accept hidden parameters (signature, job_id, today_date, session_id)
- Internal _impl functions contain the actual business logic
- ContextInjector injects parameters into kwargs (invisible to AI)

Benefits:
- AI cannot see or hallucinate signature/job_id/session_id parameters
- Cleaner tool schema focuses on trading-relevant parameters only
- Defense-in-depth: ContextInjector still overrides any provided values
- More maintainable: clear separation of public API vs internal implementation

Example AI sees:
  buy(symbol: str, amount: int) -> dict

Actual execution:
  buy(symbol="AAPL", amount=10, signature="gpt-5", job_id="...", ...)

Fixes #TBD
2025-11-02 23:34:07 -05:00
6c395f740d fix: always override context parameters in ContextInjector
Root cause: AI models were hallucinating signature/job_id/today_date values
and passing them in tool calls. The ContextInjector was checking
"if param not in request.args" before injecting, which failed when AI
provided (incorrect) values.

Fix: Always override context parameters, never trust AI-provided values.

Evidence from logs:
- ContextInjector had correct values (self.signature=gpt-5, job_id=6dabd9e6...)
- But AI was passing signature=None or hallucinated values like "fundamental-bot-v1"
- After injection, args showed the AI's (wrong) values, not the interceptor's

This ensures runtime context is ALWAYS injected regardless of what the AI sends.

Fixes #TBD
2025-11-02 23:30:49 -05:00
618943b278 debug: add self attribute logging to ContextInjector.__call__
Log ContextInjector instance ID and attribute values at entry to __call__()
to diagnose why attributes appear as None during tool invocation despite
being set correctly during set_context().

This will reveal whether:
- Multiple ContextInjector instances exist
- Attributes are being overwritten/cleared
- Wrong instance is being invoked
2025-11-02 23:17:52 -05:00
1c19eea29a debug: add comprehensive diagnostic logging for ContextInjector flow
Add instrumentation at component boundaries to trace where ContextInjector values become None:
- ModelDayExecutor: Log ContextInjector creation and set_context() invocation
- BaseAgent.set_context(): Log entry, client creation, tool reload, completion
- Includes object IDs to verify instance identity across boundaries

Part of systematic debugging investigation for issue #TBD.
2025-11-02 23:05:40 -05:00
4 changed files with 73 additions and 25 deletions

View File

@@ -232,17 +232,24 @@ class BaseAgent:
context_injector: Configured ContextInjector instance with
correct signature, today_date, job_id, session_id
"""
print(f"[DEBUG] set_context() ENTRY: Received context_injector with signature={context_injector.signature}, date={context_injector.today_date}, job_id={context_injector.job_id}, session_id={context_injector.session_id}")
self.context_injector = context_injector
print(f"[DEBUG] set_context(): Set self.context_injector, id={id(self.context_injector)}")
# Recreate MCP client with the interceptor
# Note: We need to recreate because MultiServerMCPClient doesn't have add_interceptor()
print(f"[DEBUG] set_context(): Creating new MCP client with interceptor, id={id(context_injector)}")
self.client = MultiServerMCPClient(
self.mcp_config,
tool_interceptors=[context_injector]
)
print(f"[DEBUG] set_context(): MCP client created")
# CRITICAL: Reload tools from new client so they use the interceptor
print(f"[DEBUG] set_context(): Reloading tools...")
self.tools = await self.client.get_tools()
print(f"[DEBUG] set_context(): Tools reloaded, count={len(self.tools)}")
print(f"✅ Context injected: signature={context_injector.signature}, "
f"date={context_injector.today_date}, job_id={context_injector.job_id}, "

View File

@@ -49,14 +49,16 @@ class ContextInjector:
"""
# Inject context parameters for trade tools
if request.name in ["buy", "sell"]:
# Add signature and today_date to args if not present
if "signature" not in request.args:
request.args["signature"] = self.signature
if "today_date" not in request.args:
request.args["today_date"] = self.today_date
if "job_id" not in request.args and self.job_id:
# Debug: Log self attributes BEFORE injection
print(f"[ContextInjector.__call__] ENTRY: id={id(self)}, self.signature={self.signature}, self.today_date={self.today_date}, self.job_id={self.job_id}, self.session_id={self.session_id}")
print(f"[ContextInjector.__call__] Args BEFORE injection: {request.args}")
# ALWAYS inject/override context parameters (don't trust AI-provided values)
request.args["signature"] = self.signature
request.args["today_date"] = self.today_date
if self.job_id:
request.args["job_id"] = self.job_id
if "session_id" not in request.args and self.session_id:
if self.session_id:
request.args["session_id"] = self.session_id
# Debug logging

View File

@@ -82,24 +82,13 @@ def get_current_position_from_db(job_id: str, model: str, date: str) -> Tuple[Di
conn.close()
@mcp.tool()
def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
job_id: str = None, session_id: int = None) -> Dict[str, Any]:
def _buy_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
job_id: str = None, session_id: int = None) -> Dict[str, Any]:
"""
Buy stock function - writes to SQLite database.
Internal buy implementation - accepts injected context parameters.
Args:
symbol: Stock symbol (e.g., "AAPL", "MSFT")
amount: Number of shares to buy (positive integer)
signature: Model signature (injected by ContextInjector)
today_date: Trading date YYYY-MM-DD (injected by ContextInjector)
job_id: Job UUID (injected by ContextInjector)
session_id: Trading session ID (injected by ContextInjector)
Returns:
Dict[str, Any]:
- Success: {"CASH": amount, symbol: quantity, ...}
- Failure: {"error": message, ...}
This function is not exposed to the AI model. It receives runtime context
(signature, today_date, job_id, session_id) from the ContextInjector.
"""
# Validate required parameters
if not job_id:
@@ -206,8 +195,31 @@ def buy(symbol: str, amount: int, signature: str = None, today_date: str = None,
@mcp.tool()
def sell(symbol: str, amount: int, signature: str = None, today_date: str = None,
job_id: str = None, session_id: int = None) -> Dict[str, Any]:
def buy(symbol: str, amount: int, **kwargs) -> Dict[str, Any]:
"""
Buy stock shares.
Args:
symbol: Stock symbol (e.g., "AAPL", "MSFT", "GOOGL")
amount: Number of shares to buy (positive integer)
Returns:
Dict[str, Any]:
- Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
- Failure: {"error": error_message, ...}
"""
# Extract injected parameters (added by ContextInjector, hidden from AI)
signature = kwargs.get("signature")
today_date = kwargs.get("today_date")
job_id = kwargs.get("job_id")
session_id = kwargs.get("session_id")
# Delegate to internal implementation
return _buy_impl(symbol, amount, signature, today_date, job_id, session_id)
def _sell_impl(symbol: str, amount: int, signature: str = None, today_date: str = None,
job_id: str = None, session_id: int = None) -> Dict[str, Any]:
"""
Sell stock function - writes to SQLite database.
@@ -327,6 +339,30 @@ def sell(symbol: str, amount: int, signature: str = None, today_date: str = None
conn.close()
@mcp.tool()
def sell(symbol: str, amount: int, **kwargs) -> Dict[str, Any]:
"""
Sell stock shares.
Args:
symbol: Stock symbol (e.g., "AAPL", "MSFT", "GOOGL")
amount: Number of shares to sell (positive integer)
Returns:
Dict[str, Any]:
- Success: {"CASH": remaining_cash, "SYMBOL": shares, ...}
- Failure: {"error": error_message, ...}
"""
# Extract injected parameters (added by ContextInjector, hidden from AI)
signature = kwargs.get("signature")
today_date = kwargs.get("today_date")
job_id = kwargs.get("job_id")
session_id = kwargs.get("session_id")
# Delegate to internal implementation
return _sell_impl(symbol, amount, signature, today_date, job_id, session_id)
if __name__ == "__main__":
port = int(os.getenv("TRADE_HTTP_PORT", "8002"))
mcp.run(transport="streamable-http", port=port)

View File

@@ -140,7 +140,10 @@ class ModelDayExecutor:
job_id=self.job_id,
session_id=session_id
)
logger.info(f"[DEBUG] ModelDayExecutor: Created ContextInjector with signature={self.model_sig}, date={self.date}, job_id={self.job_id}, session_id={session_id}")
logger.info(f"[DEBUG] ModelDayExecutor: Calling await agent.set_context()")
await agent.set_context(context_injector)
logger.info(f"[DEBUG] ModelDayExecutor: set_context() completed")
# Run trading session
logger.info(f"Running trading session for {self.model_sig} on {self.date}")