Improve code readability in g3-core

- streaming_parser.rs: Rename has_message_like_keys to args_contain_prose_fragments
  with improved documentation explaining the heuristic for detecting malformed
  tool calls where LLM prose leaked into JSON keys

- context_window.rs: Simplify build_thin_result_message using early return
  pattern and match expression for cleaner control flow

Agent: carmack

crates/g3-core/src/context_window.rs

@@ -646,46 +646,33 @@ Format this as a detailed but concise summary that can be used to resume the con
         tool_call_leaned_count: usize,
         chars_saved: usize,
     ) -> (String, usize) {
-        let emoji = scope.emoji();
-        let label = scope.label();
         let scope_desc = match scope {
             ThinScope::FirstThird => "",
             ThinScope::All => " across entire history",
         };
 
-        if leaned_count > 0 && tool_call_leaned_count > 0 {
-            (
-                format!(
-                    "{} Context {} at {}%: {} tool results + {} tool calls{}, ~{} chars saved",
-                    emoji, label, current_percentage, leaned_count, tool_call_leaned_count, scope_desc, chars_saved
-                ),
-                chars_saved,
-            )
-        } else if leaned_count > 0 {
-            (
-                format!(
-                    "{} Context {} at {}%: {} tool results{}, ~{} chars saved",
-                    emoji, label, current_percentage, leaned_count, scope_desc, chars_saved
-                ),
-                chars_saved,
-            )
-        } else if tool_call_leaned_count > 0 {
-            (
-                format!(
-                    "{} Context {} at {}%: {} tool calls{}, ~{} chars saved",
-                    emoji, label, current_percentage, tool_call_leaned_count, scope_desc, chars_saved
-                ),
-                chars_saved,
-            )
-        } else {
-            (
-                format!(
-                    "ℹ Context {} triggered at {}% but no large tool results or tool calls found{}",
-                    scope.error_action(), current_percentage, scope_desc
-                ),
-                0,
-            )
+        // Nothing was thinned
+        if leaned_count == 0 && tool_call_leaned_count == 0 {
+            let msg = format!(
+                "ℹ Context {} triggered at {}% but no large tool results or tool calls found{}",
+                scope.error_action(), current_percentage, scope_desc
+            );
+            return (msg, 0);
         }
+
+        // Build description of what was thinned
+        let what_thinned = match (leaned_count > 0, tool_call_leaned_count > 0) {
+            (true, true) => format!("{} tool results + {} tool calls", leaned_count, tool_call_leaned_count),
+            (true, false) => format!("{} tool results", leaned_count),
+            (false, true) => format!("{} tool calls", tool_call_leaned_count),
+            (false, false) => unreachable!(), // handled above
+        };
+
+        let msg = format!(
+            "{} Context {} at {}%: {}{}, ~{} chars saved",
+            scope.emoji(), scope.label(), current_percentage, what_thinned, scope_desc, chars_saved
+        );
+        (msg, chars_saved)
     }
 
     /// Recalculate token usage based on current conversation history

crates/g3-core/src/streaming_parser.rs

@@ -77,20 +77,23 @@ impl StreamingToolParser {
         best_start
     }
 
-    /// Validate that tool call args don't contain message-like content.
-    /// This detects malformed tool calls where agent messages got mixed into args.
-    fn has_message_like_keys(args: &serde_json::Map<String, serde_json::Value>) -> bool {
+    /// Detect malformed tool calls where LLM prose leaked into JSON keys.
+    ///
+    /// When the LLM "stutters" or mixes formats, it sometimes emits JSON where
+    /// the keys are actually fragments of conversational text rather than valid
+    /// parameter names. This heuristic catches such cases by looking for:
+    /// - Unusually long keys (>100 chars)
+    /// - Newlines in keys (never valid in JSON keys)
+    /// - Common LLM response phrases that indicate prose, not parameters
+    fn args_contain_prose_fragments(args: &serde_json::Map<String, serde_json::Value>) -> bool {
+        const PROSE_MARKERS: &[&str] = &[
+            "I'll", "Let me", "Here's", "I can", "I need", "First", "Now", "The ",
+        ];
+
         args.keys().any(|key| {
             key.len() > 100
                 || key.contains('\n')
-                || key.contains("I'll")
-                || key.contains("Let me")
-                || key.contains("Here's")
-                || key.contains("I can")
-                || key.contains("I need")
-                || key.contains("First")
-                || key.contains("Now")
-                || key.contains("The ")
+                || PROSE_MARKERS.iter().any(|marker| key.contains(marker))
         })
     }
 
@@ -172,7 +175,7 @@ impl StreamingToolParser {
                     if let Ok(tool_call) = serde_json::from_str::<ToolCall>(json_str) {
                         // Validate that args is an object with reasonable keys
                         if let Some(args_obj) = tool_call.args.as_object() {
-                            if Self::has_message_like_keys(args_obj) {
+                            if Self::args_contain_prose_fragments(args_obj) {
                                 debug!(
                                     "Detected malformed tool call with message-like keys, skipping"
                                 );
@@ -220,7 +223,7 @@ impl StreamingToolParser {
 
                     if let Ok(tool_call) = serde_json::from_str::<ToolCall>(json_str) {
                         if let Some(args_obj) = tool_call.args.as_object() {
-                            if !Self::has_message_like_keys(args_obj) {
+                            if !Self::args_contain_prose_fragments(args_obj) {
                                 debug!(
                                     "Found tool call at position {}: {:?}",
                                     abs_start, tool_call.tool