fix(agents): stop priming tool calls in schema-only structured agents

- with_structured_output binds a single tool (the schema), so a primed model emitted
  an unknown web_search call and the attempt was discarded for a free-text retry,
  costing an extra round trip and the typed output
- drop the tool-range wording from the no-tool sentiment analyst and state the
  constraint once via a shared NO_EXTERNAL_TOOLS #1130

tests/test_structured_agent_prompts.py

@@ -0,0 +1,147 @@
+"""Agents on the schema-only structured-output path must not invite tool calls (#1130).
+
+`with_structured_output` binds exactly one tool (the schema). A prompt that
+primes tool use makes models emit an unknown `web_search` call, which discards
+the structured attempt and forces a free-text retry — an extra LLM round trip
+and the loss of typed output.
+
+These assert the constraint reaches the *rendered* prompt each agent actually
+sends, not merely that the constant is referenced in the module.
+"""
+from __future__ import annotations
+
+import inspect
+from unittest.mock import MagicMock
+
+import pytest
+
+import tradingagents.agents.analysts.sentiment_analyst as sentiment
+from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager
+from tradingagents.agents.managers.research_manager import create_research_manager
+from tradingagents.agents.trader.trader import create_trader
+from tradingagents.agents.utils.structured import NO_EXTERNAL_TOOLS
+
+
+def _capturing_llm(captured: dict, result):
+    """LLM whose structured binding records the prompt it was handed."""
+    structured = MagicMock()
+    structured.invoke.side_effect = lambda prompt: (
+        captured.__setitem__("prompt", prompt) or result
+    )
+    llm = MagicMock()
+    llm.with_structured_output.return_value = structured
+    return llm
+
+
+def _prompt_text(prompt) -> str:
+    """Flatten a captured prompt (str, message list, or objects) to text."""
+    if isinstance(prompt, str):
+        return prompt
+    parts = []
+    for m in prompt:
+        parts.append(m.get("content", "") if isinstance(m, dict) else getattr(m, "content", ""))
+    return "\n".join(str(p) for p in parts)
+
+
+@pytest.mark.unit
+def test_trader_prompt_states_constraint():
+    from tradingagents.agents.schemas import TraderAction, TraderProposal
+
+    captured = {}
+    llm = _capturing_llm(captured, TraderProposal(action=TraderAction.BUY, reasoning="x"))
+    create_trader(llm)({
+        "company_of_interest": "NVDA",
+        "investment_plan": "**Recommendation**: Buy",
+    })
+    assert NO_EXTERNAL_TOOLS in _prompt_text(captured["prompt"])
+
+
+@pytest.mark.unit
+def test_research_manager_prompt_states_constraint():
+    from tradingagents.agents.schemas import PortfolioRating, ResearchPlan
+
+    captured = {}
+    llm = _capturing_llm(
+        captured,
+        ResearchPlan(
+            recommendation=PortfolioRating.BUY, rationale="x", strategic_actions="y"
+        ),
+    )
+    create_research_manager(llm)({
+        "company_of_interest": "NVDA",
+        "investment_debate_state": {
+            "history": "h", "bull_history": "b", "bear_history": "r",
+            "current_response": "", "judge_decision": "", "count": 1,
+        },
+    })
+    assert NO_EXTERNAL_TOOLS in _prompt_text(captured["prompt"])
+
+
+@pytest.mark.unit
+def test_portfolio_manager_prompt_states_constraint():
+    from tradingagents.agents.schemas import PortfolioDecision, PortfolioRating
+
+    captured = {}
+    llm = _capturing_llm(
+        captured,
+        PortfolioDecision(
+            rating=PortfolioRating.HOLD,
+            executive_summary="x",
+            investment_thesis="y",
+        ),
+    )
+    risk = {
+        "history": "h", "aggressive_history": "a", "conservative_history": "c",
+        "neutral_history": "n", "current_aggressive_response": "",
+        "current_conservative_response": "", "current_neutral_response": "",
+        "latest_speaker": "Neutral", "count": 1,
+    }
+    create_portfolio_manager(llm)({
+        "company_of_interest": "NVDA",
+        "risk_debate_state": risk,
+        "investment_plan": "plan",
+        "trader_investment_plan": "trader plan",
+    })
+    assert NO_EXTERNAL_TOOLS in _prompt_text(captured["prompt"])
+
+
+@pytest.mark.unit
+def test_sentiment_prompt_states_constraint(monkeypatch):
+    from tradingagents.agents.schemas import SentimentBand, SentimentReport
+
+    # Pre-fetched sources are stubbed so the prompt builds without network I/O.
+    monkeypatch.setattr(sentiment, "fetch_stocktwits_messages", lambda *a, **k: "st")
+    monkeypatch.setattr(sentiment, "fetch_reddit_posts", lambda *a, **k: "rd")
+    monkeypatch.setattr(sentiment.get_news, "func", lambda *a, **k: "news", raising=False)
+
+    captured = {}
+    llm = _capturing_llm(captured, SentimentReport(
+        overall_band=SentimentBand.BULLISH, overall_score=7.5,
+        confidence="high", narrative="n",
+    ))
+    sentiment.create_sentiment_analyst(llm)({
+        "company_of_interest": "NVDA", "trade_date": "2026-01-15",
+        "asset_type": "stock", "messages": [],
+    })
+    text = _prompt_text(captured["prompt"])
+    assert NO_EXTERNAL_TOOLS in text
+    # This agent binds no tools, so tool-range wording must not reappear.
+    assert "tool-call date ranges" not in text
+
+
+@pytest.mark.unit
+def test_tool_using_analysts_keep_their_date_guidance():
+    # The analysts that really do call tools keep the wording that anchors their
+    # tool date ranges (#836) — this fix is scoped to no-tool agents.
+    import tradingagents.agents.analysts.market_analyst as market
+    import tradingagents.agents.analysts.news_analyst as news
+    for module in (market, news):
+        assert "tool-call date ranges" in inspect.getsource(module)
+
+
+@pytest.mark.unit
+def test_constraint_text_is_unambiguous():
+    assert "do not call external tools" in NO_EXTERNAL_TOOLS.lower()
+    # No template braces: it is embedded in ChatPromptTemplate strings, where
+    # braces would be parsed as input variables.
+    assert "{" not in NO_EXTERNAL_TOOLS and "}" not in NO_EXTERNAL_TOOLS

tradingagents/agents/analysts/sentiment_analyst.py

@@ -36,6 +36,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_news,
 )
 from tradingagents.agents.utils.structured import (
+    NO_EXTERNAL_TOOLS,
     bind_structured,
     invoke_structured_or_freetext,
 )
@@ -86,7 +87,11 @@ def create_sentiment_analyst(llm):
                     "You are a helpful AI assistant, collaborating with other assistants."
                     " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
                     " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
-                    " Today's date is {current_date}; treat it as 'now' for all analysis and tool-call date ranges. {instrument_context}"
+                    # No tool-calling here: the data is pre-fetched into the
+                    # prompt, so tool-range wording would only invite a
+                    # hallucinated tool call (#1130).
+                    " Today's date is {current_date}; treat it as 'now' for all analysis. {instrument_context}"
+                    " " + NO_EXTERNAL_TOOLS +
                     "\n{system_message}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),

tradingagents/agents/managers/portfolio_manager.py

@@ -16,6 +16,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
 )
 from tradingagents.agents.utils.structured import (
+    NO_EXTERNAL_TOOLS,
     bind_structured,
     invoke_structured_or_freetext,
 )
@@ -61,7 +62,9 @@ def create_portfolio_manager(llm):
 
 ---
 
-Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""
+Be decisive and ground every conclusion in specific evidence from the analysts.
+
+{NO_EXTERNAL_TOOLS}{get_language_instruction()}"""
 
         final_trade_decision = invoke_structured_or_freetext(
             structured_llm,

tradingagents/agents/managers/research_manager.py

@@ -8,6 +8,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
 )
 from tradingagents.agents.utils.structured import (
+    NO_EXTERNAL_TOOLS,
     bind_structured,
     invoke_structured_or_freetext,
 )
@@ -40,7 +41,9 @@ Commit to a clear stance whenever the debate's strongest arguments warrant one;
 ---
 
 **Debate History:**
-{history}""" + get_language_instruction()
+{history}
+
+{NO_EXTERNAL_TOOLS}""" + get_language_instruction()
 
         investment_plan = invoke_structured_or_freetext(
             structured_llm,

tradingagents/agents/trader/trader.py

@@ -12,6 +12,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
 )
 from tradingagents.agents.utils.structured import (
+    NO_EXTERNAL_TOOLS,
     bind_structured,
     invoke_structured_or_freetext,
 )
@@ -32,6 +33,7 @@ def create_trader(llm):
                     "You are a trading agent analyzing market data to make investment decisions. "
                     "Based on your analysis, provide a specific recommendation to buy, sell, or hold. "
                     "Anchor your reasoning in the analysts' reports and the research plan. "
+                    + NO_EXTERNAL_TOOLS
                     + get_language_instruction()
                 ),
             },

tradingagents/agents/utils/structured.py

@@ -28,6 +28,16 @@ logger = logging.getLogger(__name__)
 
 T = TypeVar("T", bound=BaseModel)
 
+# Schema-only structured output binds exactly one tool (the schema itself), so a
+# model that reaches for a search tool emits an unknown tool call and the whole
+# structured attempt is discarded for a free-text retry. Agents on this path
+# state the constraint explicitly rather than relying on the binding alone
+# (#1130).
+NO_EXTERNAL_TOOLS = (
+    "Use only the evidence provided in this prompt. Do not call external tools "
+    "or search the web; if something is missing, say so explicitly."
+)
+
 
 def bind_structured(llm: Any, schema: type[T], agent_name: str) -> Any | None:
     """Return ``llm.with_structured_output(schema)`` or ``None`` if unsupported.