serve: end-to-end tool-calling regression test + unparsed-marker diagnosis (#401)

The gateway's tool-calling path had unit coverage (parse_tool_calls,
render_chat) but nothing exercised the real subprocess wire protocol or the
HTTP surface a coding client actually hits. #401 reports plain-text replies
where tool_calls were expected; every documented path checks out, so pin the
whole path down with a mock engine speaking SUBMIT/DATA/DONE and assert:

- non-stream: tool_calls populated, finish_reason tool_calls, no raw markers
- stream: markers suppressed across 20-way chunk splits, tool_calls delta
- tool-result round trip: <|observation|><tool_response> rendering, text reply
- no tools: plain text untouched

Also emit a stderr diagnosis when tools are declared and tool-call markers
are present in the reply but the strict parse matches nothing (typically
quantization-mangled output) pointing at COLI_TOOL_SALVAGE=1 -- the likely
field condition behind #401.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

c/openai_server.py

@@ -271,6 +271,14 @@ def parse_tool_calls(reply, tools=None):
                 salvaged.append(name)
         calls.append({"id": "call_" + uuid.uuid4().hex[:24], "type": "function",
                       "function": {"name": name, "arguments": json.dumps(args, ensure_ascii=False)}})
+    if tools and not calls and re.search(r"</?tool_call>|</?arg_key>|</?arg_value>", reply):
+        # Diagnosi per la #401: il client ha dichiarato i tools e il modello ha PROVATO la
+        # sintassi, ma il parse rigoroso non ha agganciato nulla (tipico output int4 storpiato).
+        # EN: #401 field diagnosis: tools were declared and the model attempted the syntax,
+        # EN: but the strict parse matched nothing (typically quantization-mangled output).
+        sys.stderr.write("[api] tools declared and tool-call markers present, but no call "
+                         "parsed -- output may be quantization-mangled; try COLI_TOOL_SALVAGE=1\n")
+        sys.stderr.flush()
     text = _BOX_RE.sub("", reply)
     if THINK_CLOSE in text:
         text = text.split(THINK_CLOSE, 1)[1]