Enforce the tool-call contract, asking servers only where it works

The executor ended a milestone on any reply without a tool call, and
nothing held a model to the offered tools. Measured on the 4070:
tool_choice "required" is enforced by vLLM (local-fast) and Anthropic,
but qwen3-14b (local-coder) under it cannot stop and writes until the
output ceiling, a known model bug. So enforcement is asked for per
model, and the loop holds the contract itself everywhere.

- The catalog reads supports_tool_choice from LiteLLM's model_info;
  BudgetedClient sends tool_choice only to models declared true, as
  "required" on the normalized route and {"type": "any"} on the
  Anthropic passthrough, and only when tools are offered
- The executor gains an explicit finish tool, the only way to end a
  milestone. Finishing with nothing changed is refused once; a prose
  reply gets one nudge, then escalates; a call to a tool not offered
  counts toward failure_limit, like a failed command; calls after
  finish are recorded as not run
- Clients record the model that actually answered. ModelFacts.answered
  recognises a proxy fallback, which once credited Claude's answers to
  a local model that could not call tools at all
- The context probe reports a fallback instead of trusting it; rerun,
  both local models now refuse over-long prompts, as they should
- The capability matrix checks every answer came from the model asked,
  that models call only offered tools, and that a declared
  supports_tool_choice is true
- Spec A15 and the tuning guide say how enforcement is requested,
  declared and backed up

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

nisse-dev/tests/test_probe.py

@@ -42,7 +42,9 @@ def test_probe_calibrates_then_sends_half_again_the_window() -> None:
         words = len(json.loads(request.content)["messages"][0]["content"].split())
         sizes.append(words * 2)
         reported = min(words * 2, 4096)
-        return httpx.Response(200, json={"usage": {"prompt_tokens": reported}})
+        return httpx.Response(
+            200, json={"model": "llama3.2", "usage": {"prompt_tokens": reported}}
+        )
 
     with httpx.Client(
         base_url="http://litellm", transport=httpx.MockTransport(handler)
@@ -51,3 +53,37 @@ def test_probe_calibrates_then_sends_half_again_the_window() -> None:
 
     assert result.verdict == "matches"
     assert sizes[1] >= 4096 * 1.4  # the probe really exceeded the window
+
+
+def test_a_fallback_answer_is_never_credited_to_the_model() -> None:
+    """Measured: LiteLLM answered an oversized prompt to local-coder with
+    claude-sonnet-5, and the probe called the refusing server roomy."""
+
+    def handler(request: httpx.Request) -> httpx.Response:
+        words = len(json.loads(request.content)["messages"][0]["content"].split())
+        model = "local-coder" if words < 1000 else "claude-sonnet-5"
+        return httpx.Response(
+            200, json={"model": model, "usage": {"prompt_tokens": words * 2}}
+        )
+
+    with httpx.Client(
+        base_url="http://litellm", transport=httpx.MockTransport(handler)
+    ) as http:
+        result = probe(http, "local-coder", declared=32768, backing="openai/qwen3-14b")
+
+    assert result.verdict == "fallback"
+    assert "claude-sonnet-5 answered instead of local-coder" in result.message
+
+
+def test_a_provider_reporting_its_own_model_name_is_not_a_fallback() -> None:
+    def handler(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(
+            200, json={"model": "qwen3-14b", "usage": {"prompt_tokens": 400}}
+        )
+
+    with httpx.Client(
+        base_url="http://litellm", transport=httpx.MockTransport(handler)
+    ) as http:
+        result = probe(http, "local-coder", declared=32768, backing="openai/qwen3-14b")
+
+    assert result.verdict != "fallback"