fix(compaction): don't halve context_length on output-cap-too-large errors

When the API returns "max_tokens too large given prompt" (input tokens
are within the context window, but input + requested output > window),
the old code incorrectly routed through the same handler as "prompt too
long" errors, calling get_next_probe_tier() and permanently halving
context_length. This made things worse: the window was fine, only the
requested output size needed trimming for that one call.

Two distinct error classes now handled separately:

  Prompt too long  — input itself exceeds context window.
    Fix: compress history + halve context_length (existing behaviour,
    unchanged).

  Output cap too large — input OK, but input + max_tokens > window.
    Fix: parse available_tokens from the error message, set a one-shot
    _ephemeral_max_output_tokens override for the retry, and leave
    context_length completely untouched.

Changes:
- agent/model_metadata.py: add parse_available_output_tokens_from_error()
  that detects Anthropic's "available_tokens: N" error format and returns
  the available output budget, or None for all other error types.
- run_agent.py: call the new parser first in the is_context_length_error
  block; if it fires, set _ephemeral_max_output_tokens (with a 64-token
  safety margin) and break to retry without touching context_length.
  _build_api_kwargs consumes the ephemeral value exactly once then clears
  it so subsequent calls use self.max_tokens normally.
- agent/anthropic_adapter.py: expand build_anthropic_kwargs docstring to
  clearly document the max_tokens (output cap) vs context_length (total
  window) distinction, which is a persistent source of confusion due to
  the OpenAI-inherited "max_tokens" name.
- cli-config.yaml.example: add inline comments explaining both keys side
  by side where users are most likely to look.
- website/docs/integrations/providers.md: add a callout box at the top
  of "Context Length Detection" and clarify the troubleshooting entry.
- tests/test_ctx_halving_fix.py: 24 tests across four classes covering
  the parser, build_anthropic_kwargs clamping, ephemeral one-shot
  consumption, and the invariant that context_length is never mutated
  on output-cap errors.

cli-config.yaml.example

@@ -48,6 +48,25 @@ model:
   # api_key: "your-key-here"  # Uncomment to set here instead of .env
   base_url: "https://openrouter.ai/api/v1"
 
+  # ── Token limits — two settings, easy to confuse ──────────────────────────
+  #
+  # context_length: TOTAL context window (input + output tokens combined).
+  #   Controls when Hermes compresses history and validates requests.
+  #   Leave unset — Hermes auto-detects the correct value from the provider.
+  #   Set manually only when auto-detection is wrong (e.g. a local server with
+  #   a custom num_ctx, or a proxy that doesn't expose /v1/models).
+  #
+  # context_length: 131072
+  #
+  # max_tokens: OUTPUT cap — maximum tokens the model may generate per response.
+  #   Unrelated to how long your conversation history can be.
+  #   The OpenAI-standard name "max_tokens" is a misnomer; Anthropic's native
+  #   API has since renamed it "max_output_tokens" for clarity.
+  #   Leave unset to use the model's native output ceiling (recommended).
+  #   Set only if you want to deliberately limit individual response length.
+  #
+  # max_tokens: 8192
+
 # =============================================================================
 # OpenRouter Provider Routing (only applies when using OpenRouter)
 # =============================================================================