feat: add emulated tool-calling bridge for Lingma

Add a proxy-side tool emulation layer so Lingma requests can surface stable OpenAI tool_calls and Anthropic tool_use blocks even when upstream tool events are missing or inconsistent.

Constraint: Keep native Lingma tool event bridging as the first path and layer emulation as a fallback

Rejected: Depend exclusively on Lingma native tool/invoke events | tool visibility remains inconsistent across models and transports

Confidence: high

Scope-risk: moderate

app/http/tool_bridge.py

@@ -196,6 +196,33 @@ def _extract_tool_calls_from_text(text: str) -> list[dict[str, Any]] | None:
     return None
 
 
+def _extract_hash_tool_call_event_from_text(
+    text: str,
+    *,
+    forced_tool_name: str | None = None,
+) -> dict[str, Any] | None:
+    raw = (text or "").strip()
+    if not raw:
+        return None
+    match = re.search(
+        r"#Tool Call\s*```([A-Za-z0-9_\-.]+)\s*(\{.*?\})\s*```",
+        raw,
+        flags=re.S,
+    )
+    if not match:
+        return None
+    name = match.group(1).strip()
+    if forced_tool_name and name != forced_tool_name:
+        return None
+    try:
+        arguments = json.loads(match.group(2))
+    except Exception:
+        return None
+    if not isinstance(arguments, dict):
+        return None
+    return {"name": name, "input": arguments}
+
+
 def _tool_code_single_arg_name(
     tools: list[dict[str, Any]] | None, forced_tool_name: str
 ) -> str | None:
@@ -342,6 +369,45 @@ def _forced_tool_fallback_event(
     )
 
 
+def _declared_tool_names(tools: list[dict[str, Any]] | None) -> list[str]:
+    if not isinstance(tools, list):
+        return []
+    out: list[str] = []
+    for tool in tools:
+        name = _openai_tool_name(tool) or _anthropic_tool_name(tool)
+        if name and name not in out:
+            out.append(name)
+    return out
+
+
+def _infer_tool_event_from_declared_tools(
+    text: str,
+    *,
+    tools: list[dict[str, Any]] | None,
+) -> dict[str, Any] | None:
+    for tool_name in _declared_tool_names(tools):
+        inferred = _extract_function_call_event_from_text(
+            text,
+            forced_tool_name=tool_name,
+        )
+        if inferred is not None:
+            return inferred
+        inferred = _extract_hash_tool_call_event_from_text(
+            text,
+            forced_tool_name=tool_name,
+        )
+        if inferred is not None:
+            return inferred
+        inferred = _forced_tool_fallback_event(
+            text,
+            forced_tool_name=tool_name,
+            tools=tools,
+        )
+        if inferred is not None:
+            return inferred
+    return None
+
+
 def _openai_tool_call(
     tool: dict[str, Any], *, forced_id: str | None = None
 ) -> dict[str, Any]: