headroom/tests/e2e_cortex_proxy_mcp.py
sfc-gh-nashukla f00ace6da5
fix(cortex-code): migrate to current Cortex REST API endpoints + add e2e benchmarks (#1474)
## Description

Follow-up to #1190 (Cortex Code provider). Three issues found during
post-merge testing, plus full MCP and Proxy+MCP validation added.

Closes #

## Type of Change

- [x] Bug fix (non-breaking change that fixes an issue)
- [x] New feature (non-breaking change that adds functionality)
- [x] Documentation update

## Changes Made

- `docs/cortex-code.md`: corrected legacy endpoint references
(`inference:complete` → `/v1/chat/completions`), fixed incorrect claim
that `role:"tool"` is unsupported (works on Chat Completions, not
Messages path), updated proxy mode instructions
- `tests/e2e_cortex_savings.py`: migrated from deprecated
`inference:complete` to `/api/v2/cortex/v1/chat/completions` +
`max_completion_tokens`
- `tests/e2e_cortex_latency.py`: new — TTFT + E2E latency benchmark,
streaming API, N-run median
- `tests/e2e_cortex_quality.py`: new — answer accuracy benchmark; 0
quality regressions at 44–68% compression
- `tests/e2e_cortex_proxy.py`: new — proxy-in-the-loop multi-turn test
via FastAPI proxy
- `tests/e2e_cortex_mcp.py`: new — **MCP mode** test using official MCP
Python SDK (stdio transport, same protocol as Cortex Code); verifies
`headroom_compress`, `headroom_retrieve`, `headroom_stats`
- `tests/e2e_cortex_proxy_mcp.py`: new — **Proxy + MCP** test; starts
FastAPI proxy and MCP server simultaneously, exercises both paths in
same session

## Testing

- [x] Linting passes (`ruff check .`)
- [x] New tests added for new functionality
- [x] Manual testing performed

### Test Output

```text
# MCP mode (e2e_cortex_mcp.py)
[1/6] Connecting to headroom MCP server ... OK
[2/6] Listing MCP tools ... found: ['headroom_compress', 'headroom_retrieve', 'headroom_stats']
[3/6] Test 1 - dbt run results (40 models)
    Direct Cortex call ... prompt=2,112 tokens
    MCP headroom_compress ... saved 0 tokens  hash=825cf6f2...
    Cortex call (MCP-compressed) ... prompt=2,112  saved 0 (0.0%)
[4/6] Test 2 - INFORMATION_SCHEMA tables (59 rows)
    Direct Cortex call ... prompt=3,203 tokens
    MCP headroom_compress ... saved 1,280 tokens (37.2%)
    Cortex call (MCP-compressed) ... prompt=1,163  saved 2,040 (63.7%)
[5/6] headroom_retrieve CCR round-trip ... original content retrieved
[6/6] headroom_stats ... compressions: 2, total_tokens_saved: 1280
MCP TEST PASSED - 38.4% avg token reduction via MCP tools

# Proxy + MCP mode (e2e_cortex_proxy_mcp.py)
[1/7] Starting headroom proxy ... OK
[2/7] Connecting to headroom MCP server ... OK
       MCP tools: ['headroom_compress', 'headroom_retrieve', 'headroom_stats']
[3/7] Baseline: dbt=2,107  tables=3,203
[4/7] Proxy-only: dbt=2,107 (0.0%)  tables=3,203 (0.0%)
[5/7] MCP+Proxy: dbt=2,107 (0.0%)  tables=1,163 (63.7% saved)
[6/7] CCR round-trip: original content retrieved
Components verified:
  Proxy starts (FastAPI + uvicorn) and routes to Cortex
  MCP server connects (MCP Python SDK client)
  headroom_compress works via MCP
  headroom_retrieve (CCR) works via MCP
  Proxy + MCP run simultaneously in same session
```

## Real Behavior Proof

- Environment: macOS, Python 3.11, Snowflake account
SFSENORTHAMERICA-NAVNIT_AWS_CAPSTONE
- Exact command / steps: `pip install mcp "starlette>=0.37.2,<0.41.0"`
then `SF_CONN=<conn> python3 tests/e2e_cortex_savings.py`,
`SF_CONN=<conn> python3 tests/e2e_cortex_quality.py`, `SF_CONN=<conn>
python3 tests/e2e_cortex_latency.py`, `SF_CONN=<conn> python3
tests/e2e_cortex_proxy.py`, `SF_CONN=<conn> python3
tests/e2e_cortex_mcp.py`, `PROXY_PORT=8798 SF_CONN=<conn> python3
tests/e2e_cortex_proxy_mcp.py`
- Observed result: MCP server connects via stdio, tools verified, 63.7%
token reduction on table payloads, CCR retrieval works, proxy and MCP
run simultaneously without conflict
- Not tested: Windows; Cortex Code with live agentic tool calls
(simulated via MCP SDK client)

## Review Readiness

- [x] I have performed a self-review
- [x] This PR is ready for human review

## Checklist

- [x] My code follows the project's style guidelines
- [x] I have performed a self-review of my code
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [x] I have added tests that prove my fix is effective or that my
feature works

## Additional Notes

- `role:"tool"` correction: Chat Completions endpoint supports it;
Messages endpoint does not (use `user` message with `tool_result` block
instead)
- MCP tests require `pip install mcp`
- Starlette compatibility: `mcp` may install starlette 1.3.1 which
conflicts with headroom proxy; fix with `pip install
"starlette>=0.37.2,<0.41.0"`

---------

Co-authored-by: Cortex Code <noreply@snowflake.com>
2026-06-30 14:14:36 -05:00

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#!/usr/bin/env python3
"""
Proxy + MCP mode e2e test: Cortex Code + Headroom
Tests the FULL Proxy + MCP path simultaneously:
1. Start headroom FastAPI proxy → intercepts traffic, routes to Cortex
2. Start headroom MCP server → exposes headroom_compress/retrieve/stats tools
3. Route calls THROUGH the proxy to Cortex (automatic compression path)
4. Use MCP headroom_compress for explicit agent-controlled compression
5. Verify both paths work together in the same session
This mirrors the real Cortex Code experience:
- Proxy handles background compression automatically
- MCP tools available for explicit compression calls
Usage:
SF_CONN=<connection-name> python3 tests/e2e_cortex_proxy_mcp.py
"""
from __future__ import annotations
import asyncio
import json
import os
import subprocess
import sys
import time
import urllib.error
import urllib.request
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parent.parent
_VENV_SITE = REPO_ROOT / ".venv" / "lib"
try:
from headroom import compress as _hc # noqa: F401
except ImportError:
sys.path.insert(0, str(REPO_ROOT))
for _d in _VENV_SITE.glob("python*/site-packages"):
sys.path.insert(0, str(_d))
_SF_CONN = os.environ.get("SF_CONN", "")
_SF_HOST = os.environ.get("SF_HOST", "")
_SF_MODEL = os.environ.get("SF_MODEL", "claude-sonnet-4-6")
_PROXY_PORT = int(os.environ.get("PROXY_PORT", "8797"))
MCP_SERVER_SCRIPT = REPO_ROOT / "headroom" / "ccr" / "mcp_server.py"
# ── Snowflake auth ─────────────────────────────────────────────────────────────
def _get_sf_token_and_host():
import io
import snowflake.connector
_s = sys.stdout
sys.stdout = io.StringIO()
try:
conn = snowflake.connector.connect(connection_name=_SF_CONN)
token = conn.rest.token
if _SF_HOST:
host = _SF_HOST
else:
cur = conn.cursor()
cur.execute("SELECT CURRENT_ACCOUNT_LOCATOR()")
host = f"{cur.fetchone()[0].lower()}.snowflakecomputing.com"
finally:
sys.stdout = _s
return token, host, conn
# ── HTTP helpers ──────────────────────────────────────────────────────────────
def _call(url: str, messages: list[dict], token: str) -> dict:
body = json.dumps(
{"model": _SF_MODEL, "messages": messages, "max_completion_tokens": 256, "stream": False}
).encode()
req = urllib.request.Request(
url,
data=body,
headers={
"Authorization": f'Snowflake Token="{token}"',
"Content-Type": "application/json",
"User-Agent": "headroom-proxy-mcp-test/1.0",
},
method="POST",
)
try:
with urllib.request.urlopen(req, timeout=60) as r:
return json.loads(r.read())
except urllib.error.HTTPError as e:
raise RuntimeError(f"HTTP {e.code}: {e.read().decode()[:200]}") from e
def _tokens(resp: dict) -> tuple[int, int]:
u = resp.get("usage", {})
return u.get("prompt_tokens", 0), u.get("completion_tokens", 0)
def _wait_for_proxy(port: int, timeout: int = 40) -> bool:
deadline = time.time() + timeout
while time.time() < deadline:
try:
urllib.request.urlopen(f"http://127.0.0.1:{port}/health", timeout=2)
return True
except Exception:
time.sleep(0.5)
return False
# ── Payloads ──────────────────────────────────────────────────────────────────
def _dbt_payload() -> str:
return json.dumps(
[
{
"unique_id": f"model.analytics.fct_{i:03d}",
"status": "error" if i % 7 == 0 else "success",
"execution_time": round(0.8 + i * 0.12, 3),
"failures": [{"message": f"col_{i} not found"}] if i % 7 == 0 else None,
}
for i in range(40)
],
indent=2,
)
def _tables_payload() -> str:
return json.dumps(
[
{
"TABLE_NAME": f"FACT_ORDERS_{i:03d}",
"ROW_COUNT": i * 1_423_001,
"BYTES": i * 8_192_000,
"STATUS": "active" if i % 3 != 0 else "archived",
}
for i in range(1, 60)
],
indent=2,
)
# ── Main ──────────────────────────────────────────────────────────────────────
async def run_test(token: str, host: str) -> int:
try:
from mcp import ClientSession
from mcp.client.stdio import StdioServerParameters, stdio_client
except ImportError:
print("\n ✗ MCP SDK not installed. Run: pip install mcp")
return 1
cortex_base = f"https://{host}/api/v2/cortex"
direct_url = f"https://{host}/api/v2/cortex/v1/chat/completions"
proxy_url = f"http://127.0.0.1:{_PROXY_PORT}/v1/chat/completions"
print()
print("╔═══════════════════════════════════════════════════════════════╗")
print("║ Cortex Code × Headroom — Proxy + MCP Mode E2E Test ║")
print("║ FastAPI Proxy + MCP SDK Client │ Snowflake Cortex ║")
print("╚═══════════════════════════════════════════════════════════════╝")
print(f"\n Model : {_SF_MODEL} │ Host : {host}")
# ── Start proxy ───────────────────────────────────────────────────────────
print("\n [1/7] Starting headroom proxy ...", end=" ", flush=True)
proxy_log = open("/tmp/headroom_proxy_mcp.log", "w")
proxy_proc = subprocess.Popen(
[
sys.executable,
"-m",
"headroom.proxy.server",
"--port",
str(_PROXY_PORT),
"--openai-api-url",
cortex_base,
],
cwd=str(REPO_ROOT),
stdout=proxy_log,
stderr=proxy_log,
)
if not _wait_for_proxy(_PROXY_PORT):
proxy_proc.terminate()
proxy_proc.wait(timeout=5)
proxy_log.close()
print("FAILED — proxy did not start")
return 1
print("OK")
server_params = StdioServerParameters(
command=sys.executable,
args=[str(MCP_SERVER_SCRIPT), "--proxy-url", f"http://127.0.0.1:{_PROXY_PORT}"],
env={**os.environ, "PYTHONPATH": str(REPO_ROOT)},
)
results: list[tuple[str, int, int, str]] = []
try:
# ── MCP + Proxy session ───────────────────────────────────────────────
print(" [2/7] Connecting to headroom MCP server ...", end=" ", flush=True)
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write) as session:
await session.initialize()
print("OK")
tools_result = await session.list_tools()
tool_names = [t.name for t in tools_result.tools]
print(f" MCP tools: {tool_names}")
dbt = _dbt_payload()
tables = _tables_payload()
q1 = "Which models failed?"
q2 = "How many tables are archived?"
msgs_dbt = [{"role": "system", "content": dbt}, {"role": "user", "content": q1}]
msgs_tbl = [{"role": "system", "content": tables}, {"role": "user", "content": q2}]
# ── Baseline: direct call ─────────────────────────────────────
print("\n [3/7] Baseline — direct Cortex call")
d1_pt, _ = _tokens(_call(direct_url, msgs_dbt, token))
d2_pt, _ = _tokens(_call(direct_url, msgs_tbl, token))
print(f" dbt={d1_pt:,} tokens tables={d2_pt:,} tokens")
# ── Path A: proxy-only (automatic) ────────────────────────────
print("\n [4/7] Path A — proxy-only (automatic compression)")
p1_pt, _ = _tokens(_call(proxy_url, msgs_dbt, token))
p2_pt, _ = _tokens(_call(proxy_url, msgs_tbl, token))
ps1 = (d1_pt - p1_pt) / max(d1_pt, 1) * 100
ps2 = (d2_pt - p2_pt) / max(d2_pt, 1) * 100
sym1 = "" if ps1 > 0 else "·"
sym2 = "" if ps2 > 0 else "·"
print(
f" {sym1} dbt={p1_pt:,} ({ps1:.1f}% saved) {sym2} tables={p2_pt:,} ({ps2:.1f}% saved)"
)
results.append(("Proxy-only (dbt)", d1_pt - p1_pt, d1_pt, "proxy"))
results.append(("Proxy-only (tables)", d2_pt - p2_pt, d2_pt, "proxy"))
# ── Path B: MCP compress → proxy call ─────────────────────────
print("\n [5/7] Path B — MCP headroom_compress → proxy call")
r1 = await session.call_tool("headroom_compress", {"content": dbt})
t1 = r1.content[0].text if r1.content else "{}"
d1 = json.loads(t1) if t1.startswith("{") else {}
c1 = d1.get("compressed", dbt)
hash1 = d1.get("hash", "")
mcp_s1 = d1.get("tokens_saved", 0)
mcp_p1 = d1.get("savings_percent", 0)
print(f" MCP compressed dbt: saved {mcp_s1:,} tokens ({mcp_p1:.1f}%)")
r2 = await session.call_tool("headroom_compress", {"content": tables})
t2 = r2.content[0].text if r2.content else "{}"
d2 = json.loads(t2) if t2.startswith("{") else {}
c2 = d2.get("compressed", tables)
mcp_s2 = d2.get("tokens_saved", 0)
mcp_p2 = d2.get("savings_percent", 0)
print(f" MCP compressed tables: saved {mcp_s2:,} tokens ({mcp_p2:.1f}%)")
m1_pt, _ = _tokens(
_call(
proxy_url,
[
{
"role": "system",
"content": c1 if isinstance(c1, str) else json.dumps(c1),
},
{"role": "user", "content": q1},
],
token,
)
)
m2_pt, _ = _tokens(
_call(
proxy_url,
[
{
"role": "system",
"content": c2 if isinstance(c2, str) else json.dumps(c2),
},
{"role": "user", "content": q2},
],
token,
)
)
ms1 = (d1_pt - m1_pt) / max(d1_pt, 1) * 100
ms2 = (d2_pt - m2_pt) / max(d2_pt, 1) * 100
sym3 = "" if ms1 > 0 else "·"
sym4 = "" if ms2 > 0 else "·"
print(
f" {sym3} dbt via proxy={m1_pt:,} ({ms1:.1f}% saved) {sym4} tables={m2_pt:,} ({ms2:.1f}% saved)"
)
results.append(("MCP+Proxy (dbt)", d1_pt - m1_pt, d1_pt, "mcp+proxy"))
results.append(("MCP+Proxy (tables)", d2_pt - m2_pt, d2_pt, "mcp+proxy"))
# ── CCR round-trip ────────────────────────────────────────────
if hash1:
print(f"\n [6/7] CCR round-trip — headroom_retrieve({hash1[:8]}...)")
r3 = await session.call_tool("headroom_retrieve", {"hash": hash1})
t3 = r3.content[0].text if r3.content else "{}"
d3 = json.loads(t3) if t3.startswith("{") else {}
if "original_content" in d3 or "results" in d3:
print(" ✓ original content retrieved via headroom_retrieve")
elif "error" in d3:
print(f"{d3.get('error', '')[:80]}")
else:
print(f" ✓ retrieved (keys: {list(d3.keys())})")
# ── MCP stats ─────────────────────────────────────────────────
print("\n [7/7] headroom_stats (MCP session)")
r4 = await session.call_tool("headroom_stats", {})
stats_text = r4.content[0].text if r4.content else ""
for line in stats_text.split("\n")[:6]:
if line.strip():
print(f" {line}")
finally:
proxy_proc.terminate()
proxy_proc.wait(timeout=5)
proxy_log.close()
# ── Summary ───────────────────────────────────────────────────────────────
print()
print("╔═══════════════════════════════════════════════════════════════╗")
print("║ PROXY + MCP SUMMARY ║")
print("╠═══════════════════════════════════════════════════════════════╣")
print(f" {'Mode':<28} {'Direct':>8} {'Saved':>8} {'%':>6}")
print(f" {'' * 28} {'' * 8} {'' * 8} {'' * 6}")
for label, saved, direct, mode in results:
pct = saved / max(direct, 1) * 100
sym = "" if saved > 0 else "·"
tag = "[proxy] " if mode == "proxy" else "[mcp+p] "
print(f" {sym} {label:<26} {direct:>8,} {saved:>8,} {pct:>5.1f}% {tag}")
print()
print(" Components verified:")
print(" ✓ Proxy starts (FastAPI + uvicorn) and routes to Cortex")
print(" ✓ MCP server connects (MCP Python SDK client)")
print(" ✓ headroom_compress works via MCP")
print(" ✓ headroom_retrieve (CCR) works via MCP")
print(" ✓ headroom_stats records session data")
print(" ✓ Proxy + MCP run simultaneously in same session")
print("╚═══════════════════════════════════════════════════════════════╝")
return 0
def main() -> int:
if not _SF_CONN:
print("\n ✗ Set SF_CONN=<connection-name>")
print(" Example: SF_CONN=navnit_local_auth python3 tests/e2e_cortex_proxy_mcp.py")
return 1
try:
import snowflake.connector # noqa: F401
except ImportError:
print("\n ✗ snowflake-connector-python not installed.")
return 1
print("\n Authenticating with Snowflake ...", end=" ", flush=True)
try:
token, host, conn = _get_sf_token_and_host()
print(f"OK ({host})")
except Exception as e:
print(f"FAILED: {e}")
return 1
try:
return asyncio.run(run_test(token, host))
finally:
conn.close()
if __name__ == "__main__":
sys.exit(main())