Fix MLX pythonic tool parser metadata

README.md

@@ -47,13 +47,13 @@ Stage 2 was a local MLX LoRA repair pass focused on native LFM tool-call emissio
 
 ## Why The Repair Pass Was Needed
 
-Hermes executes OpenAI-style structured `tool_calls`. The model often emitted tool calls as assistant text in native LFM syntax instead:
+Hermes executes OpenAI-style structured `tool_calls`. During the first local setup, MLX returned native tool calls as assistant text because the exported tokenizer config did not declare the MLX tool parser:
 
 ```text
 <|tool_call_start|>[browser_navigate(url="https://www.google.com/search?q=weather+Austin+TX")]<|tool_call_end|>
 ```
 
-That is semantically a tool call, but Hermes did not execute it until a small parser patch converted the text block into structured tool-call objects. The repair pass was trained and evaluated with that parser patch disabled so the model itself had to emit valid native LFM calls.
+That is semantically a tool call, but Hermes does not execute it if it arrives only as `message.content`. The fix is to set `tool_parser_type: "pythonic"` in `tokenizer_config.json`, which activates MLX's built-in parser for `<|tool_call_start|>[fn(...)]<|tool_call_end|>` and returns real `message.tool_calls`. A small Hermes parser patch remains useful as a safety net for runtimes or model variants that still return native tool syntax as plain text.
 
 ## Key Eval Results
 
@@ -67,6 +67,8 @@ That is semantically a tool call, but Hermes did not execute it until a small pa
 
 Both 8-bit and 6-bit variants also passed a five-prompt generation smoke test with no observed repetition collapse.
 
+After adding `tool_parser_type: "pythonic"`, a live MLX server call with a browser tool returned structured OpenAI-compatible `message.tool_calls` and `finish_reason: "tool_calls"` for the repaired fused model.
+
 ## Repository Contents
 
 - `configs/`: run configs and local hardware profile.
@@ -82,4 +84,4 @@ Both 8-bit and 6-bit variants also passed a five-prompt generation smoke test wi
 - The eval suite is intentionally small and targeted. Passing it means the release gate was met; it does not prove broad agent reliability.
 - The grouped direct-weight stage updated MoE experts and routers, not every model weight simultaneously.
 - The 6-bit release uses MLX whole-checkpoint quantization because stock MLX conversion does not currently expose the exact expert-only mixed quant policy used in the experimental runtime.
-- The Hermes parser patch remains useful for production reliability because many local models emit valid tool calls as text rather than structured OpenAI `tool_calls`.
+- The Hermes parser patch remains useful for production reliability when a runtime returns valid tool calls as text rather than structured OpenAI `tool_calls`.