fix(adapters): stop dropping non-streaming tool calls (agent loops died on turn 2)

Four adapters handled tool_calls in transform_stream_chunk but lost them in
transform_response, so any NON-streaming tool-using conversation broke on its
second request: the client received finish_reason:"tool_calls" with no
tool_calls payload, replayed an assistant message whose function
name/arguments were empty, and the upstream rejected the next turn with

    400 invalid tool_call function, function/name/arguments cannot be empty

The production audit trail shows 46 such failures on sensenova alone.

- sensenova.lua: forward message.tool_calls, decoding the arguments JSON string
  into an object as the unified shape expects.
- gemini.lua: collect functionCall parts from candidates[].content.parts. Also
  correct finish_reason, since Gemini reports "STOP" even when it emitted a
  function call and clients keyed on it treat that as a finished answer.
- ollama.lua: the field was initialized to an empty table and never filled;
  fill it and likewise correct done_reason "stop" -> "tool_calls".

trae is a different failure with the same symptom: trae-local-api's OpenAI
endpoint (/v1/chat/completions, src/server.js:353) never reads the request's
`tools` array — only its Anthropic endpoint does — so the relayed model is never
told the tool schema and instead PRINTS a <tool_call>{...}</tool_call> block into
content, leaving message.tool_calls null and finish_reason "stop". An OpenAI
client sees an ordinary completion and its agent loop ends mid-conversation.
trae.lua now recovers the structured call from that text, strips the block from
user-visible content, and corrects finish_reason. Both tag spellings
(<tool_call>/<toolcall>, the latter is what the same codebase's Anthropic prompt
asks for) and all three argument key names (arguments/params/input) are accepted.
This is a defensive fallback: fixing the upstream shim to honour `tools` remains
the real fix, since the model still guesses parameter names.

Tests: TestNonStreamToolCallsPreserved covers all ten OpenAI-shaped adapters,
TestGeminiNonStreamToolCalls and TestOllamaNonStreamToolCalls cover their native
shapes, TestTraeTextToolCallRecovery covers both tag spellings, prose around the
block, and asserts a plain text answer never gains tool_calls.

Verified end-to-end against mock upstreams reproducing each shape: a full
two-round agent loop (tool call -> tool result -> final answer) now completes for
both the structured and the text-emitted variants.

internal/lua/adapters/ollama.lua

@@ -58,13 +58,39 @@ function adapter.transform_response(raw_body)
     local unified = {
         content = "",
         finish_reason = resp.done_reason or "",
-        tool_calls = {},
         -- key must be token_usage to match Go's UnifiedResponse json tag
         token_usage = { prompt = p, completion = c, total = p + c }
     }
 
     if resp.message then
         unified.content = resp.message.content or ""
+        -- Non-streaming tool calls were previously dropped: the field was
+        -- initialized to an empty table and never filled, while
+        -- transform_stream_chunk handled them. A non-streaming agent turn thus
+        -- looked like a plain answer and the tool loop stopped.
+        if type(resp.message.tool_calls) == "table" and #resp.message.tool_calls > 0 then
+            local tcs = {}
+            for _, tc in ipairs(resp.message.tool_calls) do
+                local fn = tc["function"] or {}
+                -- Ollama sends arguments as an object already
+                local args = fn.arguments
+                if type(args) == "string" then
+                    local aok, decoded = pcall(json.decode, args)
+                    args = aok and decoded or {}
+                elseif type(args) ~= "table" then
+                    args = {}
+                end
+                table.insert(tcs, {
+                    id = tc.id or ("call_" .. #tcs),
+                    type = tc.type or "function",
+                    name = fn.name or "",
+                    arguments = args
+                })
+            end
+            unified.tool_calls = tcs
+            -- Ollama reports done_reason "stop" alongside tool calls
+            unified.finish_reason = "tool_calls"
+        end
     end
 
     return json.encode(unified)