refactor: break up MCPBridge.run() into focused helper methods

Extract connection setup/message building, tool-call round processing,
and the multi-round loop into three private helpers:
- _build_initial_messages() — static, constructs prompt message list
- _process_round_tool_calls() — executes one round of tool calls
- _run_tool_loop() — drives the multi-round loop, returns result tuple

run() is now ~35 lines of logic (down from 129), with error recovery
consolidated in a single return at the end.

Fixes #1135

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/timmy/mcp_bridge.py

@@ -399,6 +399,74 @@ class MCPBridge:
             logger.warning("Tool '%s' execution failed: %s", name, exc)
             return f"Error executing {name}: {exc}"
 
+    @staticmethod
+    def _build_initial_messages(
+        prompt: str, system_prompt: str | None
+    ) -> list[dict]:
+        """Build the initial message list for a run."""
+        messages: list[dict] = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+        messages.append({"role": "user", "content": prompt})
+        return messages
+
+    async def _process_round_tool_calls(
+        self,
+        messages: list[dict],
+        model_tool_calls: list[dict],
+        rounds: int,
+        tool_calls_made: list[dict],
+    ) -> None:
+        """Execute all tool calls in one round, appending results to messages."""
+        for tc in model_tool_calls:
+            func = tc.get("function", {})
+            tool_name = func.get("name", "unknown")
+            tool_args = func.get("arguments", {})
+            logger.info(
+                "Bridge tool call [round %d]: %s(%s)",
+                rounds,
+                tool_name,
+                tool_args,
+            )
+            result = await self._execute_tool_call(tc)
+            tool_calls_made.append(
+                {
+                    "round": rounds,
+                    "tool": tool_name,
+                    "arguments": tool_args,
+                    "result": result[:500],  # Truncate for logging
+                }
+            )
+            messages.append({"role": "tool", "content": result})
+
+    async def _run_tool_loop(
+        self, messages: list[dict], tools: list[dict]
+    ) -> tuple[str, list[dict], int, str]:
+        """Run the tool-call loop until final response or max rounds reached.
+
+        Returns:
+            Tuple of (content, tool_calls_made, rounds, error).
+        """
+        tool_calls_made: list[dict] = []
+        rounds = 0
+
+        for round_num in range(self.max_rounds):
+            rounds = round_num + 1
+            response = await self._chat(messages, tools)
+            msg = response.get("message", {})
+            model_tool_calls = msg.get("tool_calls", [])
+
+            if not model_tool_calls:
+                return msg.get("content", ""), tool_calls_made, rounds, ""
+
+            messages.append(msg)
+            await self._process_round_tool_calls(
+                messages, model_tool_calls, rounds, tool_calls_made
+            )
+
+        error = f"Exceeded maximum of {self.max_rounds} tool-call rounds"
+        return "(max tool-call rounds reached)", tool_calls_made, rounds, error
+
     async def run(
         self,
         prompt: str,
@@ -419,115 +487,37 @@ class MCPBridge:
             BridgeResult with the final response and tool call history.
         """
         start = time.time()
-        messages: list[dict] = []
-
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-
-        messages.append({"role": "user", "content": prompt})
-
+        messages = self._build_initial_messages(prompt, system_prompt)
         tools = self._build_ollama_tools()
         tool_calls_made: list[dict] = []
         rounds = 0
+        error_msg = ""
 
         try:
-            for round_num in range(self.max_rounds):
-                rounds = round_num + 1
-                response = await self._chat(messages, tools)
-                msg = response.get("message", {})
-
-                # Check if model made tool calls
-                model_tool_calls = msg.get("tool_calls", [])
-                if not model_tool_calls:
-                    # Final text response — done.
-                    content = msg.get("content", "")
-                    latency = (time.time() - start) * 1000
-                    return BridgeResult(
-                        content=content,
-                        tool_calls_made=tool_calls_made,
-                        rounds=rounds,
-                        latency_ms=latency,
-                        model=self.model,
-                    )
-
-                # Append the assistant message (with tool_calls) to history
-                messages.append(msg)
-
-                # Execute each tool call and add results
-                for tc in model_tool_calls:
-                    func = tc.get("function", {})
-                    tool_name = func.get("name", "unknown")
-                    tool_args = func.get("arguments", {})
-
-                    logger.info(
-                        "Bridge tool call [round %d]: %s(%s)",
-                        rounds,
-                        tool_name,
-                        tool_args,
-                    )
-
-                    result = await self._execute_tool_call(tc)
-                    tool_calls_made.append(
-                        {
-                            "round": rounds,
-                            "tool": tool_name,
-                            "arguments": tool_args,
-                            "result": result[:500],  # Truncate for logging
-                        }
-                    )
-
-                    # Add tool result to message history
-                    messages.append(
-                        {
-                            "role": "tool",
-                            "content": result,
-                        }
-                    )
-
-            # Hit max rounds
-            latency = (time.time() - start) * 1000
-            return BridgeResult(
-                content="(max tool-call rounds reached)",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Exceeded maximum of {self.max_rounds} tool-call rounds",
+            content, tool_calls_made, rounds, error_msg = await self._run_tool_loop(
+                messages, tools
             )
-
         except httpx.ConnectError as exc:
-            latency = (time.time() - start) * 1000
             logger.warning("Ollama connection failed: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Ollama connection failed: {exc}",
-            )
+            error_msg = f"Ollama connection failed: {exc}"
+            content = ""
         except httpx.HTTPStatusError as exc:
-            latency = (time.time() - start) * 1000
             logger.warning("Ollama HTTP error: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=f"Ollama HTTP error: {exc.response.status_code}",
-            )
+            error_msg = f"Ollama HTTP error: {exc.response.status_code}"
+            content = ""
         except Exception as exc:
-            latency = (time.time() - start) * 1000
             logger.error("MCPBridge run failed: %s", exc)
-            return BridgeResult(
-                content="",
-                tool_calls_made=tool_calls_made,
-                rounds=rounds,
-                latency_ms=latency,
-                model=self.model,
-                error=str(exc),
-            )
+            error_msg = str(exc)
+            content = ""
+
+        return BridgeResult(
+            content=content,
+            tool_calls_made=tool_calls_made,
+            rounds=rounds,
+            latency_ms=(time.time() - start) * 1000,
+            model=self.model,
+            error=error_msg,
+        )
 
     def status(self) -> dict:
         """Return bridge status for the dashboard."""