Check for MITM-captured function calls BEFORE emitting text in the
streaming handler. This prevents the dummy 'Tool call completed'
placeholder (sent to the LS) from leaking to OpenCode, which was
confusing it into infinite loops.
Also removes duplicate function call storage at end of response loop
since they're now stored immediately when detected.
- Accept tools and tool_choice fields in CompletionRequest
- Convert OpenAI tools to Gemini format and store in MitmStore
- Detect MITM-captured function calls in streaming poll loop
- Emit tool_calls delta chunks in OpenAI streaming format
- Finish with 'tool_calls' reason instead of 'stop' when tools used
- Only clear tools when request has none (prevents stale state leak)
Tool definitions stored in MitmStore from /v1/responses requests were
persisting and getting injected into /v1/chat/completions requests.
This caused Gemini to return functionCalls instead of text, and since
the completions handler has no function call handling logic, it would
poll forever waiting for text that never came.
Fix: clear active_tools, active_tool_config, and has_active_function_call
at the start of handle_completions. Also add clear_active_function_call()
method to MitmStore.
- store.rs: Add tool context storage (active tools, tool config, pending
tool results, call_id mapping, last function calls for history rewrite)
- types.rs: Add tools/tool_choice fields to ResponsesRequest, add
build_function_call_output helper for OpenAI function_call output items
- modify.rs: Replace hardcoded get_weather with dynamic ToolContext
injection. Add openai_tools_to_gemini and openai_tool_choice_to_gemini
converters. Add conversation history rewriting for tool result turns
(replaces fake 'Tool call completed' model turn with real functionCall,
injects functionResponse before last user turn)
- proxy.rs: Build ToolContext from MitmStore before calling modify_request.
Save last_function_calls for history rewriting on subsequent turns
- responses.rs: Store client tools in MitmStore before LS call. Detect
function_call_output in input array for tool result submission. Return
captured functionCalls as OpenAI function_call output items with
generated call_ids and stringified arguments
- gemini.rs: New Gemini-native endpoint (POST /v1/gemini) with zero
format translation. Accepts functionDeclarations directly, returns
functionCall in Gemini format directly
- mod.rs: Wire /v1/gemini route, bump version to 3.3.0
When MITM strips LS tools and injects custom tools:
- Google returns functionCall → captured in MitmStore
- Follow-up LS requests are blocked with fake SSE response
- Proxy consumes captured calls and clears the flag
- Result: 1 real Google API call instead of 5+ per tool call
Flow: Client → Proxy → LS → MITM(inject tool) → Google
Google returns functionCall → MITM captures it
LS tries follow-up → MITM blocks (fake response)
Proxy reads captured functionCall → returns to client
- Subscribe to StreamCascadeReactiveUpdates for real-time cascade state diffs
- Fall back to timer-based polling if streaming RPC unavailable
- Remove StreamCascadePanelReactiveUpdates code (dead end, only has plan_status/user_settings)
- Remove debug diff file-saving code
- Add stream_reactive_rpc() helper to backend
Streaming poll: 800-1200ms → 150-250ms (5x faster)
Sync poll: 1000-1800ms → 200-400ms (4x faster)
Verified via STEP_DUMP instrumentation that the LS updates
plannerResponse.response incrementally during GENERATING status,
so faster polling yields smoother progressive text delivery.
Also restructured streaming to emit reasoning events first
when thinking content is detected in LS steps before response text.
Adds proper streaming SSE events for reasoning content:
- response.output_item.added (reasoning)
- response.reasoning_summary_part.added
- response.reasoning_summary_text.delta
- response.reasoning_summary_text.done
- response.reasoning_summary_part.done
- response.output_item.done (reasoning)
These are emitted before the message events, matching the format
that OpenAI-compatible clients expect for displaying thinking content.
The LS makes two Google API calls for thinking models. Call 2 (thinking
summary) may not have arrived by the time usage_from_poll runs after
Call 1 (response). Now we peek first, and if thinking tokens exist but
text is missing, wait up to 1s for the merge to happen.
Also adds peek_usage method to MitmStore for non-consuming reads.
The LS strips thinking/reasoning text from plannerResponse steps —
only the thinkingSignature (opaque verification blob) is preserved.
The actual thinking text flows through the MITM proxy in the raw
Google SSE response (parts with thought: true) and Anthropic SSE
(thinking_delta content blocks).
Changes:
- StreamingAccumulator now accumulates thinking text from SSE events
- ApiUsage gains thinking_text: Option<String>
- usage_from_poll returns (Usage, Option<thinking_text>)
- Thinking text priority: MITM-captured > LS-extracted (fallback)
- Reasoning output item now populated from real API data
- Removed debug dump code
Thinking content was previously returned as non-standard top-level
fields (thinking, thinking_duration). Now follows the official OpenAI
Responses API format:
- Reasoning appears as a 'type: reasoning' item in the output array
with summary[].text containing the thinking content
- Message item follows after the reasoning item
- thinking_signature kept as proxy extension (internal multi-turn data)
- Removed ResponseOutput/OutputContent structs in favor of
serde_json::Value for polymorphic output items
When the MITM can't extract a cascade ID from the intercepted request
(Content-Length: 0 / chunked encoding), usage is stored under '_latest'.
Now usage_from_poll and completions try the exact cascade_id first,
then fall back to '_latest' so MITM-captured tokens are actually used.
