mirror of
https://github.com/headroomlabs-ai/headroom.git
synced 2026-08-27 14:17:10 -04:00
e4a41faa33
- Fix E402: Move module-level imports to top of file - Fix F401: Add noqa for availability check imports - Fix F402: Rename loop variables shadowing imports - Fix E722: Replace bare except with except Exception - Fix B904: Add exception chaining (from e) - Fix F811: Remove duplicate imports - Fix B027: Add noqa for empty close() method - Fix E741: Rename ambiguous variable l -> label - Fix I001: Import sorting issues - Apply ruff format to all 106 files All 902 tests pass.
120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
"""Demonstrate Headroom MCP compression on real-world tool outputs.
|
|
|
|
Run with:
|
|
PYTHONPATH=. python -m examples.mcp_demo.show_compression
|
|
"""
|
|
|
|
import random
|
|
|
|
from headroom.integrations.mcp import (
|
|
compress_tool_result_with_metrics,
|
|
)
|
|
from headroom.providers import OpenAIProvider
|
|
|
|
from .mock_mcp_servers import (
|
|
generate_database_query_results,
|
|
generate_github_issues_results,
|
|
generate_log_search_results,
|
|
generate_slack_search_results,
|
|
)
|
|
|
|
|
|
def main():
|
|
random.seed(42)
|
|
|
|
print("\n" + "=" * 70)
|
|
print("HEADROOM MCP INTEGRATION - COMPRESSION DEMO")
|
|
print("=" * 70)
|
|
|
|
# Get token counter
|
|
provider = OpenAIProvider()
|
|
provider.get_token_counter("gpt-4o")
|
|
|
|
# Test scenarios
|
|
scenarios = [
|
|
{
|
|
"name": "Slack Search",
|
|
"tool_name": "mcp__slack__search_messages",
|
|
"tool_args": {"query": "production errors", "limit": 150},
|
|
"user_query": "find production errors from last week",
|
|
"content": generate_slack_search_results("production errors", count=150),
|
|
},
|
|
{
|
|
"name": "Database Query",
|
|
"tool_name": "mcp__database__query",
|
|
"tool_args": {"sql": "SELECT * FROM users WHERE status != 'active'"},
|
|
"user_query": "find users with issues",
|
|
"content": generate_database_query_results("users", count=200),
|
|
},
|
|
{
|
|
"name": "Log Analysis",
|
|
"tool_name": "mcp__logs__search",
|
|
"tool_args": {"service": "api-gateway", "level": "ERROR"},
|
|
"user_query": "find errors in api-gateway",
|
|
"content": generate_log_search_results("api-gateway", count=300),
|
|
},
|
|
{
|
|
"name": "GitHub Issues",
|
|
"tool_name": "mcp__github__list_issues",
|
|
"tool_args": {"repo": "myorg/myrepo", "state": "open"},
|
|
"user_query": "find open bugs",
|
|
"content": generate_github_issues_results("myorg/myrepo", count=100),
|
|
},
|
|
]
|
|
|
|
total_before = 0
|
|
total_after = 0
|
|
|
|
for scenario in scenarios:
|
|
print(f"\n{'─' * 70}")
|
|
print(f"Tool: {scenario['name']}")
|
|
print(f"MCP Server: {scenario['tool_name']}")
|
|
print(f'User Query: "{scenario["user_query"]}"')
|
|
print(f"{'─' * 70}")
|
|
|
|
result = compress_tool_result_with_metrics(
|
|
content=scenario["content"],
|
|
tool_name=scenario["tool_name"],
|
|
tool_args=scenario["tool_args"],
|
|
user_query=scenario["user_query"],
|
|
)
|
|
|
|
print(f"\n Original tokens: {result.original_tokens:>8,}")
|
|
print(f" Compressed tokens: {result.compressed_tokens:>8,}")
|
|
print(f" Tokens saved: {result.tokens_saved:>8,} ({result.compression_ratio:.1%})")
|
|
|
|
if result.items_before and result.items_after:
|
|
print(f"\n Items before: {result.items_before:>8}")
|
|
print(f" Items after: {result.items_after:>8}")
|
|
print(f" Errors preserved: {result.errors_preserved:>8}")
|
|
|
|
total_before += result.original_tokens
|
|
total_after += result.compressed_tokens
|
|
|
|
# Summary
|
|
print("\n" + "=" * 70)
|
|
print("SUMMARY")
|
|
print("=" * 70)
|
|
print(f"\n Total original tokens: {total_before:>8,}")
|
|
print(f" Total compressed tokens: {total_after:>8,}")
|
|
print(f" Total tokens saved: {total_before - total_after:>8,}")
|
|
print(f" Overall compression: {(total_before - total_after) / total_before:.1%}")
|
|
|
|
# Cost savings at GPT-4o rates ($2.50/1M input)
|
|
cost_before = total_before * 2.50 / 1_000_000
|
|
cost_after = total_after * 2.50 / 1_000_000
|
|
print(f"\n Cost before (GPT-4o): ${cost_before:.4f}")
|
|
print(f" Cost after (GPT-4o): ${cost_after:.4f}")
|
|
print(f" Cost saved per request: ${cost_before - cost_after:.4f}")
|
|
|
|
# At scale
|
|
daily_requests = 1000
|
|
monthly_savings = (cost_before - cost_after) * daily_requests * 30
|
|
print(f"\n At {daily_requests:,} requests/day:")
|
|
print(f" Monthly savings: ${monthly_savings:,.2f}")
|
|
|
|
print("\n" + "=" * 70)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|