diff --git a/headroom/proxy/handlers/gemini.py b/headroom/proxy/handlers/gemini.py
index b3c1d7adb..6dd8e92fe 100644
--- a/headroom/proxy/handlers/gemini.py
+++ b/headroom/proxy/handlers/gemini.py
@@ -108,7 +108,11 @@ class GeminiHandlerMixin:
         return result
 
     def _gemini_contents_to_messages(
-        self, contents: list[dict], system_instruction: dict | None = None
+        self,
+        contents: list[dict],
+        system_instruction: dict | None = None,
+        *,
+        include_function_responses: bool = False,
     ) -> tuple[list[dict], set[int]]:
         """Convert Gemini contents[] format to OpenAI messages[] format for optimization.
 
@@ -119,6 +123,12 @@ class GeminiHandlerMixin:
         OpenAI format:
             messages: [{"role": "user", "content": "..."}]
 
+        When include_function_responses is True, functionResponse payloads are
+        additionally emitted as ``role="tool"`` messages so waste-signal
+        detection can see tool output (#819). That richer list is telemetry-only:
+        entries with non-text parts stay in preserved_indices and are restored
+        verbatim, so it must never be used as the compression input.
+
         Returns:
             Tuple of (messages, preserved_indices) where preserved_indices contains
             the indices of content entries that have non-text parts (images, function
@@ -151,8 +161,29 @@ class GeminiHandlerMixin:
             if text_parts:
                 messages.append({"role": role, "content": "\n".join(text_parts)})
 
+            if include_function_responses:
+                for part in parts:
+                    if "functionResponse" not in part:
+                        continue
+                    payload = self._function_response_text(part["functionResponse"])
+                    if payload:
+                        messages.append({"role": "tool", "content": payload})
+
         return messages, preserved_indices
 
+    @staticmethod
+    def _function_response_text(function_response: dict) -> str:
+        """Serialize a functionResponse payload for waste-signal parsing."""
+        response = function_response.get("response")
+        if response is None:
+            return ""
+        if isinstance(response, str):
+            return response
+        try:
+            return json.dumps(response, ensure_ascii=False, default=str)
+        except (TypeError, ValueError):
+            return str(response)
+
     def _messages_to_gemini_contents(self, messages: list[dict]) -> tuple[list[dict], dict | None]:
         """Convert OpenAI messages[] format back to Gemini contents[] format.
 
@@ -446,11 +477,17 @@ class GeminiHandlerMixin:
             try:
                 # Use OpenAI pipeline (similar message format)
                 context_limit = self.openai_provider.get_context_limit(model)
+                # Richer conversion incl. functionResponse payloads so tool
+                # output reaches waste-signal detection (#819); telemetry-only.
+                waste_messages, _ = self._gemini_contents_to_messages(
+                    contents, system_instruction, include_function_responses=True
+                )
                 result = self.openai_pipeline.apply(
                     messages=messages,
                     model=model,
                     model_limit=context_limit,
                     context=extract_user_query(messages),
+                    waste_messages=waste_messages,
                 )
                 if result.messages != messages:
                     optimized_messages = result.messages
@@ -792,11 +829,17 @@ class GeminiHandlerMixin:
         if _decision.should_compress:
             try:
                 context_limit = self.openai_provider.get_context_limit(model)
+                # Richer conversion incl. functionResponse payloads so tool
+                # output reaches waste-signal detection (#819); telemetry-only.
+                waste_messages, _ = self._gemini_contents_to_messages(
+                    contents, system_instruction, include_function_responses=True
+                )
                 result = self.openai_pipeline.apply(
                     messages=messages,
                     model=model,
                     model_limit=context_limit,
                     context=extract_user_query(messages),
+                    waste_messages=waste_messages,
                 )
                 if result.messages != messages:
                     optimized_messages = result.messages
diff --git a/headroom/transforms/pipeline.py b/headroom/transforms/pipeline.py
index e278ebe6b..973fc30bf 100644
--- a/headroom/transforms/pipeline.py
+++ b/headroom/transforms/pipeline.py
@@ -212,11 +212,14 @@ class TransformPipeline:
                 - output_buffer: Output buffer override.
                 - tool_profiles: Per-tool compression profiles.
                 - request_id: Optional request ID for diff artifact.
+                - waste_messages: Optional richer conversion of the same request
+                  used for waste-signal detection only (never transformed).
 
         Returns:
             Combined TransformResult.
         """
         record_metrics = kwargs.pop("record_metrics", True)
+        waste_messages = kwargs.pop("waste_messages", None)
         tokenizer = self._get_tokenizer(model)
         provider_name = self._provider_name()
 
@@ -430,13 +433,17 @@ class TransformPipeline:
                     transforms=transform_diffs,
                 )
 
-            # Detect waste signals in original messages (only when significant compression)
+            # Detect waste signals in original messages (only when significant
+            # compression). Handlers whose wire format carries tool output the
+            # message conversion drops (e.g. Gemini functionResponse parts, #819)
+            # pass a richer waste_messages list that is parsed instead — it is
+            # telemetry-only and never transformed.
             waste_signals: WasteSignals | None = None
             if tokens_before > tokens_after and (tokens_before - tokens_after) > 100:
                 try:
                     from ..parser import parse_messages
 
-                    _, _, waste_signals = parse_messages(messages, tokenizer)
+                    _, _, waste_signals = parse_messages(waste_messages or messages, tokenizer)
                     if waste_signals.total() == 0:
                         waste_signals = None
                 except Exception:
diff --git a/tests/test_gemini_function_response_waste.py b/tests/test_gemini_function_response_waste.py
new file mode 100644
index 000000000..638db4f8a
--- /dev/null
+++ b/tests/test_gemini_function_response_waste.py
@@ -0,0 +1,203 @@
+"""Gemini functionResponse waste-signal visibility (issue #819).
+
+Gemini ``functionResponse`` parts are preserved verbatim on the wire (never
+compressed), but their payloads previously never reached ``parse_messages``,
+so tool output — where most waste lives — contributed nothing to waste
+detection on the Gemini paths.
+
+The fix is telemetry-only:
+
+1. ``_gemini_contents_to_messages(..., include_function_responses=True)``
+   additionally emits each functionResponse payload as a ``role="tool"``
+   message.
+2. ``TransformPipeline.apply(..., waste_messages=...)`` parses that richer
+   list for waste signals instead of the transform input. The transform path
+   and token accounting are untouched.
+"""
+
+from __future__ import annotations
+
+import json
+
+import pytest
+
+pytest.importorskip("fastapi")
+pytest.importorskip("httpx")
+
+from headroom import OpenAIProvider, Tokenizer
+from headroom.config import HeadroomConfig
+from headroom.parser import parse_messages
+from headroom.proxy.server import HeadroomProxy, ProxyConfig
+from headroom.transforms.pipeline import TransformPipeline
+
+_provider = OpenAIProvider()
+
+
+@pytest.fixture
+def proxy() -> HeadroomProxy:
+    config = ProxyConfig(
+        optimize=False,
+        cache_enabled=False,
+        rate_limit_enabled=False,
+        cost_tracking_enabled=False,
+    )
+    return HeadroomProxy(config)
+
+
+@pytest.fixture
+def tokenizer() -> Tokenizer:
+    return Tokenizer(_provider.get_token_counter("gpt-4o"), "gpt-4o")
+
+
+def _big_payload(rows: int = 200) -> dict:
+    return {
+        "result": [
+            {"id": i, "name": f"item_{i}", "status": "ok", "score": i * 3.14} for i in range(rows)
+        ]
+    }
+
+
+def _function_response_content(payload: object, name: str = "fetch_data") -> dict:
+    return {
+        "role": "user",
+        "parts": [{"functionResponse": {"name": name, "response": payload}}],
+    }
+
+
+class TestFunctionResponseConversion:
+    def test_default_conversion_emits_no_tool_messages(self, proxy):
+        contents = [
+            {"role": "user", "parts": [{"text": "fetch the data"}]},
+            _function_response_content(_big_payload()),
+        ]
+        messages, preserved = proxy._gemini_contents_to_messages(contents)
+        assert [m["role"] for m in messages] == ["user"]
+        assert preserved == {1}
+
+    def test_flag_emits_tool_message_for_dict_response(self, proxy):
+        payload = _big_payload()
+        contents = [
+            {"role": "user", "parts": [{"text": "fetch the data"}]},
+            _function_response_content(payload),
+        ]
+        messages, preserved = proxy._gemini_contents_to_messages(
+            contents, include_function_responses=True
+        )
+        assert [m["role"] for m in messages] == ["user", "tool"]
+        assert json.loads(messages[1]["content"]) == payload
+        # preserved_indices semantics unchanged: the entry is still restored
+        # verbatim on the wire regardless of the telemetry conversion.
+        assert preserved == {1}
+
+    def test_flag_passes_string_response_through(self, proxy):
+        contents = [_function_response_content("plain text tool output")]
+        messages, _ = proxy._gemini_contents_to_messages(contents, include_function_responses=True)
+        assert messages == [{"role": "tool", "content": "plain text tool output"}]
+
+    def test_flag_skips_missing_response(self, proxy):
+        contents = [
+            {"role": "user", "parts": [{"functionResponse": {"name": "noop"}}]},
+            {"role": "user", "parts": [{"functionResponse": {"name": "none", "response": None}}]},
+        ]
+        messages, _ = proxy._gemini_contents_to_messages(contents, include_function_responses=True)
+        assert messages == []
+
+    def test_flag_emits_text_before_tool_within_entry(self, proxy):
+        contents = [
+            {
+                "role": "user",
+                "parts": [
+                    {"text": "tool said:"},
+                    {"functionResponse": {"name": "f", "response": "output"}},
+                ],
+            }
+        ]
+        messages, _ = proxy._gemini_contents_to_messages(contents, include_function_responses=True)
+        assert [m["role"] for m in messages] == ["user", "tool"]
+        assert messages[0]["content"] == "tool said:"
+        assert messages[1]["content"] == "output"
+
+    def test_unserializable_response_falls_back_to_str(self, proxy):
+        circular: dict = {"name": "loop"}
+        circular["self"] = circular
+        text = proxy._function_response_text({"response": circular})
+        assert "loop" in text
+
+
+class TestFunctionResponseWasteParsing:
+    def test_function_response_payload_reaches_waste_signals(self, proxy, tokenizer):
+        contents = [
+            {"role": "user", "parts": [{"text": "fetch the data"}]},
+            _function_response_content(_big_payload()),
+        ]
+        messages, _ = proxy._gemini_contents_to_messages(contents, include_function_responses=True)
+        blocks, _, waste = parse_messages(messages, tokenizer)
+        assert any(b.kind == "tool_result" for b in blocks)
+        assert waste.json_bloat_tokens > 0
+
+    def test_repeated_function_response_counts_as_reread(self, proxy, tokenizer):
+        payload = _big_payload()
+        filler = [{"role": "user", "parts": [{"text": f"working on step {i}"}]} for i in range(5)]
+        contents = [
+            _function_response_content(payload),
+            *filler,
+            _function_response_content(payload),
+        ]
+        messages, _ = proxy._gemini_contents_to_messages(contents, include_function_responses=True)
+        _, _, waste = parse_messages(messages, tokenizer)
+        assert waste.reread_tokens > 0
+
+
+class TestPipelineWasteMessages:
+    @staticmethod
+    def _base_messages() -> list[dict]:
+        # Compressible enough that the pipeline clears the >100 saved-token
+        # gate that guards waste-signal detection.
+        return [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Inspect the data set."},
+            {"role": "tool", "content": json.dumps(_big_payload(400)["result"])},
+        ]
+
+    def test_waste_messages_override_waste_source(self, tokenizer):
+        messages = self._base_messages()
+        extra_tool = {"role": "tool", "content": json.dumps(_big_payload(300))}
+
+        baseline = TransformPipeline(HeadroomConfig()).apply(
+            [dict(m) for m in messages], model="gpt-4o", model_limit=128000
+        )
+        enriched = TransformPipeline(HeadroomConfig()).apply(
+            [dict(m) for m in messages],
+            model="gpt-4o",
+            model_limit=128000,
+            waste_messages=[*messages, extra_tool],
+        )
+
+        assert baseline.waste_signals is not None
+        assert enriched.waste_signals is not None
+        assert enriched.waste_signals.json_bloat_tokens > baseline.waste_signals.json_bloat_tokens
+
+    def test_waste_messages_do_not_affect_transform_output(self, tokenizer):
+        messages = self._base_messages()
+        extra_tool = {"role": "tool", "content": json.dumps(_big_payload(300))}
+
+        baseline = TransformPipeline(HeadroomConfig()).apply(
+            [dict(m) for m in messages], model="gpt-4o", model_limit=128000
+        )
+        enriched = TransformPipeline(HeadroomConfig()).apply(
+            [dict(m) for m in messages],
+            model="gpt-4o",
+            model_limit=128000,
+            waste_messages=[*messages, extra_tool],
+        )
+
+        assert enriched.messages == baseline.messages
+        assert enriched.tokens_before == baseline.tokens_before
+        assert enriched.tokens_after == baseline.tokens_after
+
+    def test_no_waste_messages_falls_back_to_transform_input(self, tokenizer):
+        result = TransformPipeline(HeadroomConfig()).apply(
+            [dict(m) for m in self._base_messages()], model="gpt-4o", model_limit=128000
+        )
+        assert result.waste_signals is not None
+        assert result.waste_signals.json_bloat_tokens > 0