#!/usr/bin/env python3
"""
Test suite for scientific_research tool.
Validates both operations and all parameters, plus type error fix.
"""
import asyncio
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))
from tests.mcp_client import create_client
async def test_academic_no_type_error():
"""Critical test: verify type error fix works (citationCount sorting)."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{"operation": "academic_search", "query": "machine learning", "max_results": 3},
)
assert result.content, "No content returned"
output = result.content[0].text
# Most critical: must not have type error
assert "bad operand type" not in output.lower(), "TYPE ERROR STILL PRESENT!"
assert "Error" not in output or "paper" in output.lower() or "academic" in output.lower()
print("✅ Type error fix verified - No 'bad operand' error")
async def test_academic_search_default():
"""Test academic_search with default parameters."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research", {"operation": "academic_search", "query": "neural networks"}
)
output = result.content[0].text
assert len(output) > 0, "Empty academic search output"
print(f"✅ Academic search default test passed - {len(output)} chars")
async def test_academic_search_max_results():
"""Test academic_search with different max_results values."""
async with create_client() as client:
for max_results in [3, 10]:
result = await client.call_tool(
"scientific_research",
{
"operation": "academic_search",
"query": "deep learning",
"max_results": max_results,
},
)
output = result.content[0].text
assert len(output) > 200, f"Output too short for max_results={max_results}"
print(f" ✓ max_results={max_results} works")
print("✅ max_results parameter test passed")
async def test_academic_search_arxiv_source():
"""Test academic_search with arXiv source."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{
"operation": "academic_search",
"query": "transformer models",
"max_results": 5,
"sources": ["arxiv"],
},
)
output = result.content[0].text
assert len(output) > 200, "arXiv search output too minimal"
print("✅ arXiv source test passed")
async def test_academic_search_semantic_scholar():
"""Test academic_search with Semantic Scholar source."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{
"operation": "academic_search",
"query": "computer vision",
"max_results": 5,
"sources": ["semantic_scholar"],
},
)
output = result.content[0].text
assert len(output) > 100, "Semantic Scholar search output too minimal"
print("✅ Semantic Scholar source test passed")
async def test_academic_search_multiple_sources():
"""Test academic_search with multiple sources."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{
"operation": "academic_search",
"query": "reinforcement learning",
"max_results": 5,
"sources": ["arxiv", "semantic_scholar"],
},
)
output = result.content[0].text
assert len(output) > 200, "Multiple sources search output too minimal"
print("✅ Multiple sources test passed")
async def test_dataset_discovery_default():
"""Test dataset_discovery with default parameters."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{"operation": "dataset_discovery", "query": "image classification"},
)
output = result.content[0].text
assert len(output) > 0, "Empty dataset discovery output"
print("✅ Dataset discovery default test passed")
async def test_dataset_discovery_max_results():
"""Test dataset_discovery with specific max_results."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{
"operation": "dataset_discovery",
"query": "natural language processing",
"max_results": 5,
},
)
output = result.content[0].text
assert len(output) > 100, "Dataset discovery output too minimal"
print("✅ Dataset discovery max_results test passed")
async def test_dataset_discovery_with_categories():
"""Test dataset_discovery with categories parameter."""
async with create_client() as client:
result = await client.call_tool(
"scientific_research",
{
"operation": "dataset_discovery",
"query": "time series",
"max_results": 3,
"categories": ["computer_science"],
},
)
output = result.content[0].text
assert len(output) > 50, "Dataset discovery with categories too minimal"
print("✅ Dataset discovery with categories test passed")
if __name__ == "__main__":
asyncio.run(test_academic_no_type_error())
asyncio.run(test_academic_search_default())
asyncio.run(test_academic_search_max_results())
asyncio.run(test_academic_search_arxiv_source())
asyncio.run(test_academic_search_semantic_scholar())
asyncio.run(test_academic_search_multiple_sources())
asyncio.run(test_dataset_discovery_default())
asyncio.run(test_dataset_discovery_max_results())
asyncio.run(test_dataset_discovery_with_categories())
print("\n✅ All scientific_research tests passed!")
