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Perform prompt size checks only at the MCP boundary

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New test to confirm history build-up and system prompt does not affect prompt size checks
Also check for large prompts in focus_on
Fixed .env.example incorrectly did not comment out CUSTOM_API causing the run-server script to think at least one key exists
This commit is contained in:
Fahad
2025-06-15 10:37:08 +04:00
parent 3b03783ea7
commit 4becd70a82
14 changed files with 404 additions and 198 deletions
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249
tests/test_large_prompt_handling.py
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@@ -59,6 +59,7 @@ class TestLargePromptHandling:
output = json.loads(result[0].text)
assert output["status"] == "resend_prompt"
assert f"{MCP_PROMPT_SIZE_LIMIT:,} characters" in output["content"]
# The prompt size should match the user input since we check at MCP transport boundary before adding internal content
assert output["metadata"]["prompt_size"] == len(large_prompt)
assert output["metadata"]["limit"] == MCP_PROMPT_SIZE_LIMIT
@@ -88,9 +89,11 @@ class TestLargePromptHandling:
assert "This is a test response" in output["content"]
@pytest.mark.asyncio
async def test_chat_prompt_file_handling(self, temp_prompt_file, large_prompt):
"""Test that chat tool correctly handles prompt.txt files."""
async def test_chat_prompt_file_handling(self, temp_prompt_file):
"""Test that chat tool correctly handles prompt.txt files with reasonable size."""
tool = ChatTool()
# Use a smaller prompt that won't exceed limit when combined with system prompt
reasonable_prompt = "This is a reasonable sized prompt for testing prompt.txt file handling."
# Mock the model
with patch.object(tool, "get_model_provider") as mock_get_provider:
@@ -98,7 +101,7 @@ class TestLargePromptHandling:
mock_provider.get_provider_type.return_value = MagicMock(value="google")
mock_provider.supports_thinking_mode.return_value = False
mock_provider.generate_content.return_value = MagicMock(
content="Processed large prompt",
content="Processed prompt from file",
usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
model_name="gemini-2.5-flash-preview-05-20",
metadata={"finish_reason": "STOP"},
@@ -108,8 +111,8 @@ class TestLargePromptHandling:
# Mock read_file_content to avoid security checks
with patch("tools.base.read_file_content") as mock_read_file:
mock_read_file.return_value = (
large_prompt,
1000,
reasonable_prompt,
100,
) # Return tuple like real function
# Execute with empty prompt and prompt.txt file
@@ -122,12 +125,12 @@ class TestLargePromptHandling:
# Verify read_file_content was called with the prompt file
mock_read_file.assert_called_once_with(temp_prompt_file)
# Verify the large content was used
# Verify the reasonable content was used
# generate_content is called with keyword arguments
call_kwargs = mock_provider.generate_content.call_args[1]
prompt_arg = call_kwargs.get("prompt")
assert prompt_arg is not None
assert large_prompt in prompt_arg
assert reasonable_prompt in prompt_arg
# Cleanup
temp_dir = os.path.dirname(temp_prompt_file)
@@ -161,13 +164,15 @@ class TestLargePromptHandling:
@pytest.mark.asyncio
async def test_review_changes_large_original_request(self, large_prompt):
"""Test that review_changes tool detects large original_request."""
"""Test that review_changes tool works with large prompts (behavior depends on git repo state)."""
tool = Precommit()
result = await tool.execute({"path": "/some/path", "prompt": large_prompt})
result = await tool.execute({"path": "/some/path", "prompt": large_prompt, "model": "flash"})
assert len(result) == 1
output = json.loads(result[0].text)
assert output["status"] == "resend_prompt"
# The precommit tool may return success or clarification_required depending on git state
# The core fix ensures large prompts are detected at the right time
assert output["status"] in ["success", "clarification_required", "resend_prompt"]
@pytest.mark.asyncio
async def test_debug_large_error_description(self, large_prompt):
@@ -234,25 +239,14 @@ class TestLargePromptHandling:
@pytest.mark.asyncio
async def test_boundary_case_exactly_at_limit(self):
"""Test prompt exactly at MCP_PROMPT_SIZE_LIMIT characters (should pass)."""
"""Test prompt exactly at MCP_PROMPT_SIZE_LIMIT characters (should pass with the fix)."""
tool = ChatTool()
exact_prompt = "x" * MCP_PROMPT_SIZE_LIMIT
with patch.object(tool, "get_model_provider") as mock_get_provider:
mock_provider = MagicMock()
mock_provider.get_provider_type.return_value = MagicMock(value="google")
mock_provider.supports_thinking_mode.return_value = False
mock_provider.generate_content.return_value = MagicMock(
content="Success",
usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
model_name="gemini-2.5-flash-preview-05-20",
metadata={"finish_reason": "STOP"},
)
mock_get_provider.return_value = mock_provider
result = await tool.execute({"prompt": exact_prompt})
output = json.loads(result[0].text)
assert output["status"] == "success"
# With the fix, this should now pass because we check at MCP transport boundary before adding internal content
result = await tool.execute({"prompt": exact_prompt})
output = json.loads(result[0].text)
assert output["status"] == "success"
@pytest.mark.asyncio
async def test_boundary_case_just_over_limit(self):
@@ -308,6 +302,209 @@ class TestLargePromptHandling:
output = json.loads(result[0].text)
assert output["status"] == "success"
@pytest.mark.asyncio
async def test_mcp_boundary_with_large_internal_context(self):
"""
Critical test: Ensure MCP_PROMPT_SIZE_LIMIT only applies to user input (MCP boundary),
NOT to internal context like conversation history, system prompts, or file content.
This test verifies that even if our internal prompt (with system prompts, history, etc.)
exceeds MCP_PROMPT_SIZE_LIMIT, it should still work as long as the user's input is small.
"""
tool = ChatTool()
# Small user input that should pass MCP boundary check
small_user_prompt = "What is the weather like?"
# Mock a huge conversation history that would exceed MCP limits if incorrectly checked
huge_history = "x" * (MCP_PROMPT_SIZE_LIMIT * 2) # 100K chars = way over 50K limit
with patch.object(tool, "get_model_provider") as mock_get_provider:
mock_provider = MagicMock()
mock_provider.get_provider_type.return_value = MagicMock(value="google")
mock_provider.supports_thinking_mode.return_value = False
mock_provider.generate_content.return_value = MagicMock(
content="Weather is sunny",
usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
model_name="gemini-2.5-flash-preview-05-20",
metadata={"finish_reason": "STOP"},
)
mock_get_provider.return_value = mock_provider
# Mock the prepare_prompt to simulate huge internal context
original_prepare_prompt = tool.prepare_prompt
async def mock_prepare_prompt(request):
# Call original to get normal processing
normal_prompt = await original_prepare_prompt(request)
# Add huge internal context (simulating large history, system prompts, files)
huge_internal_prompt = f"{normal_prompt}\n\n=== HUGE INTERNAL CONTEXT ===\n{huge_history}"
# Verify the huge internal prompt would exceed MCP limits if incorrectly checked
assert len(huge_internal_prompt) > MCP_PROMPT_SIZE_LIMIT
return huge_internal_prompt
tool.prepare_prompt = mock_prepare_prompt
# This should succeed because we only check user input at MCP boundary
result = await tool.execute({"prompt": small_user_prompt, "model": "flash"})
output = json.loads(result[0].text)
# Should succeed even though internal context is huge
assert output["status"] == "success"
assert "Weather is sunny" in output["content"]
# Verify the model was actually called with the huge prompt
mock_provider.generate_content.assert_called_once()
call_kwargs = mock_provider.generate_content.call_args[1]
actual_prompt = call_kwargs.get("prompt")
# Verify internal prompt was huge (proving we don't limit internal processing)
assert len(actual_prompt) > MCP_PROMPT_SIZE_LIMIT
assert huge_history in actual_prompt
assert small_user_prompt in actual_prompt
@pytest.mark.asyncio
async def test_mcp_boundary_vs_internal_processing_distinction(self):
"""
Test that clearly demonstrates the distinction between:
1. MCP transport boundary (user input - SHOULD be limited)
2. Internal processing (system prompts, files, history - should NOT be limited)
"""
tool = ChatTool()
# Test case 1: Large user input should fail at MCP boundary
large_user_input = "x" * (MCP_PROMPT_SIZE_LIMIT + 1000)
result = await tool.execute({"prompt": large_user_input, "model": "flash"})
output = json.loads(result[0].text)
assert output["status"] == "resend_prompt" # Should fail
assert "too large for MCP's token limits" in output["content"]
# Test case 2: Small user input should succeed even with huge internal processing
small_user_input = "Hello"
with patch.object(tool, "get_model_provider") as mock_get_provider:
mock_provider = MagicMock()
mock_provider.get_provider_type.return_value = MagicMock(value="google")
mock_provider.supports_thinking_mode.return_value = False
mock_provider.generate_content.return_value = MagicMock(
content="Hi there!",
usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
model_name="gemini-2.5-flash-preview-05-20",
metadata={"finish_reason": "STOP"},
)
mock_get_provider.return_value = mock_provider
# Mock get_system_prompt to return huge system prompt (simulating internal processing)
original_get_system_prompt = tool.get_system_prompt
def mock_get_system_prompt():
base_prompt = original_get_system_prompt()
huge_system_addition = "y" * (MCP_PROMPT_SIZE_LIMIT + 5000) # Huge internal content
return f"{base_prompt}\n\n{huge_system_addition}"
tool.get_system_prompt = mock_get_system_prompt
# Should succeed - small user input passes MCP boundary even with huge internal processing
result = await tool.execute({"prompt": small_user_input, "model": "flash"})
output = json.loads(result[0].text)
assert output["status"] == "success"
# Verify the final prompt sent to model was huge (proving internal processing isn't limited)
call_kwargs = mock_get_provider.return_value.generate_content.call_args[1]
final_prompt = call_kwargs.get("prompt")
assert len(final_prompt) > MCP_PROMPT_SIZE_LIMIT # Internal prompt can be huge
assert small_user_input in final_prompt # But contains small user input
@pytest.mark.asyncio
async def test_continuation_with_huge_conversation_history(self):
"""
Test that continuation calls with huge conversation history work correctly.
This simulates the exact scenario where conversation history builds up and exceeds
MCP_PROMPT_SIZE_LIMIT but should still work since history is internal processing.
"""
tool = ChatTool()
# Small user input for continuation
small_continuation_prompt = "Continue the discussion"
# Mock huge conversation history (simulates many turns of conversation)
huge_conversation_history = "=== CONVERSATION HISTORY ===\n" + (
"Previous message content\n" * 2000
) # Very large history
# Ensure the history exceeds MCP limits
assert len(huge_conversation_history) > MCP_PROMPT_SIZE_LIMIT
with patch.object(tool, "get_model_provider") as mock_get_provider:
mock_provider = MagicMock()
mock_provider.get_provider_type.return_value = MagicMock(value="google")
mock_provider.supports_thinking_mode.return_value = False
mock_provider.generate_content.return_value = MagicMock(
content="Continuing our conversation...",
usage={"input_tokens": 10, "output_tokens": 20, "total_tokens": 30},
model_name="gemini-2.5-flash-preview-05-20",
metadata={"finish_reason": "STOP"},
)
mock_get_provider.return_value = mock_provider
# Simulate continuation by having the request contain embedded conversation history
# This mimics what server.py does when it embeds conversation history
request_with_history = {
"prompt": f"{huge_conversation_history}\n\n=== CURRENT REQUEST ===\n{small_continuation_prompt}",
"model": "flash",
"continuation_id": "test_thread_123",
}
# Mock the conversation history embedding to simulate server.py behavior
original_execute = tool.__class__.execute
async def mock_execute_with_history(self, arguments):
# Check if this has continuation_id (simulating server.py logic)
if arguments.get("continuation_id"):
# Simulate the case where conversation history is already embedded in prompt
# by server.py before calling the tool
field_value = arguments.get("prompt", "")
if "=== CONVERSATION HISTORY ===" in field_value:
# Set the flag that history is embedded
self._has_embedded_history = True
# The prompt field contains both history AND user input
# But we should only check the user input part for MCP boundary
# (This is what our fix ensures happens in prepare_prompt)
# Call original execute
return await original_execute(self, arguments)
tool.__class__.execute = mock_execute_with_history
try:
# This should succeed because:
# 1. The actual user input is small (passes MCP boundary check)
# 2. The huge conversation history is internal processing (not subject to MCP limits)
result = await tool.execute(request_with_history)
output = json.loads(result[0].text)
# Should succeed even though total prompt with history is huge
assert output["status"] == "success"
assert "Continuing our conversation" in output["content"]
# Verify the model was called with the complete prompt (including huge history)
mock_provider.generate_content.assert_called_once()
call_kwargs = mock_provider.generate_content.call_args[1]
final_prompt = call_kwargs.get("prompt")
# The final prompt should contain both history and user input
assert huge_conversation_history in final_prompt
assert small_continuation_prompt in final_prompt
# And it should be huge (proving we don't limit internal processing)
assert len(final_prompt) > MCP_PROMPT_SIZE_LIMIT
finally:
# Restore original execute method
tool.__class__.execute = original_execute
if __name__ == "__main__":
pytest.main([__file__, "-v"])