feat: default to qwen2.5-vl-3b and cap tool schema length constraints

- Default LLM_MODEL to qwen2.5-vl-3b across agent, task worker, tests,
  compose, workflow, and docs
- Cap min/max length-style constraints in tool schemas to 1000 before
  LLM requests; llama.cpp rejects grammar repetitions >= 2000 and the
  memory MCP's update_note body maxLength (200000) broke qwen2.5-vl-3b
  with 'Failed to initialize samplers: failed to parse grammar'

agent/agent.py

@@ -45,7 +45,7 @@ LLM_BASE_URL = (
 LLM_MODEL = (
     os.environ.get("LLM_MODEL")
     or os.environ.get("GEMMA_MODEL")
-    or "gemma-4-e4b"
+    or "qwen2.5-vl-3b"
 )
 LLM_API_KEY = (
     os.environ.get("LLM_API_KEY")
@@ -216,6 +216,28 @@ except ImportError:
 
 # ── OpenAI-compatible LLM with reasoning_content fallback ───────────────────
 
+# llama.cpp derives a GBNF grammar from tool schemas to constrain the model's
+# tool-call output. Its parser rejects repetition counts >= 2000 (e.g. a string
+# with maxLength 200000 emits "char{0,200000}" which fails to parse), so cap
+# the length-style constraints the voice pipeline exposes. qwen2.5-vl-3b hits
+# this; gemma happens to tolerate the same schemas.
+_LLM_SCHEMA_LENGTH_CEILING = 1000
+_LLM_SCHEMA_LENGTH_KEYS = ("minLength", "maxLength", "minItems", "maxItems")
+
+
+def _sanitize_tool_schema(node: object) -> None:
+    """Cap length-style constraints in a JSON-schema dict (mutates in place)."""
+    if isinstance(node, dict):
+        for key in _LLM_SCHEMA_LENGTH_KEYS:
+            value = node.get(key)
+            if isinstance(value, (int, float)) and value > _LLM_SCHEMA_LENGTH_CEILING:
+                node[key] = _LLM_SCHEMA_LENGTH_CEILING
+        for value in node.values():
+            _sanitize_tool_schema(value)
+    elif isinstance(node, list):
+        for item in node:
+            _sanitize_tool_schema(item)
+
 
 class _ReasoningFallbackWrapper:
     """Wraps an LLMStream to fall back to ``reasoning_content`` when the model
@@ -352,6 +374,15 @@ class HopeLLM(openai.LLM):
         idle_for = now - self._last_call_time
         self._last_call_time = now
 
+        # Cap length-style constraints on tool schemas. llama.cpp builds a GBNF
+        # grammar from these and rejects repetitions >= 2000 (qwen2.5-vl-3b
+        # fails on schemas like the memory MCP's update_note body maxLength).
+        for tool in tools or []:
+            info = getattr(tool, "info", None)
+            raw_schema = getattr(info, "raw_schema", None)
+            if isinstance(raw_schema, dict):
+                _sanitize_tool_schema(raw_schema)
+
         # Compact if context is large, OR if we've been idle for 5+ minutes
         # (user came back after a break — summarize the old conversation).
         ctx_len = len(chat_ctx.items)