test_mcp_server.py•9.03 kB
"""
Tests for MCP server tools.
"""
import pytest
import json
from unittest.mock import AsyncMock
from constrained_opt_mcp.server.main import (
solve_constraint_satisfaction,
solve_convex_optimization,
solve_linear_programming,
solve_constraint_programming,
solve_portfolio_optimization,
)
class TestMCPServerTools:
"""Test cases for MCP server tool functionality."""
@pytest.mark.asyncio
async def test_solve_constraint_satisfaction(self):
"""Test constraint satisfaction tool."""
variables = [
{"name": "x", "type": "integer"},
{"name": "y", "type": "integer"},
]
constraints = [
"x + y == 10",
"x - y == 2",
]
result = await solve_constraint_satisfaction(
variables=variables,
constraints=constraints,
description="Test problem"
)
assert len(result) == 1
assert result[0].type == "text"
data = json.loads(result[0].text)
assert data["is_satisfiable"]
assert data["values"]["x"] == 6
assert data["values"]["y"] == 4
@pytest.mark.asyncio
async def test_solve_convex_optimization(self):
"""Test convex optimization tool."""
variables = [{"name": "x", "shape": 2}]
objective_type = "minimize"
objective_expr = "cp.sum_squares(x)"
constraints = [
"x >= 0",
"cp.sum(x) == 1",
]
result = await solve_convex_optimization(
variables=variables,
objective_type=objective_type,
objective_expr=objective_expr,
constraints=constraints,
description="Test convex problem"
)
assert len(result) == 1
assert result[0].type == "text"
data = json.loads(result[0].text)
assert data["status"] == "optimal"
assert "values" in data
assert "objective_value" in data
@pytest.mark.asyncio
async def test_solve_linear_programming(self):
"""Test linear programming tool."""
sense = "minimize"
objective_coeffs = [1.0, 2.0]
variables = [
{"name": "x1", "lb": 0, "ub": 10, "type": "cont"},
{"name": "x2", "lb": 0, "ub": 10, "type": "cont"},
]
constraint_matrix = [[1, 1], [1, -1]]
constraint_senses = ["<=", ">="]
rhs_values = [5.0, 1.0]
result = await solve_linear_programming(
sense=sense,
objective_coeffs=objective_coeffs,
variables=variables,
constraint_matrix=constraint_matrix,
constraint_senses=constraint_senses,
rhs_values=rhs_values,
description="Test LP problem"
)
assert len(result) == 1
assert result[0].type == "text"
data = json.loads(result[0].text)
assert data["status"] == "optimal"
assert "values" in data
assert "objective_value" in data
@pytest.mark.asyncio
async def test_solve_constraint_programming(self):
"""Test constraint programming tool."""
variables = [
{"name": "x", "type": "integer", "domain": [0, 10]},
{"name": "y", "type": "integer", "domain": [0, 10]},
]
constraints = [
"model.add(x + y == 10)",
"model.add(x - y >= 2)",
]
objective = {
"type": "minimize",
"expression": "x + y"
}
result = await solve_constraint_programming(
variables=variables,
constraints=constraints,
objective=objective,
description="Test CP problem"
)
assert len(result) == 1
assert result[0].type == "text"
data = json.loads(result[0].text)
assert data["is_feasible"]
assert "values" in data
@pytest.mark.asyncio
async def test_solve_portfolio_optimization(self):
"""Test portfolio optimization tool."""
assets = ["Bonds", "Stocks", "RealEstate", "Commodities"]
expected_returns = [0.08, 0.12, 0.10, 0.15]
risk_factors = [0.02, 0.15, 0.08, 0.20]
correlation_matrix = [
[1.0, 0.2, 0.3, 0.1],
[0.2, 1.0, 0.6, 0.7],
[0.3, 0.6, 1.0, 0.5],
[0.1, 0.7, 0.5, 1.0],
]
max_allocations = [0.4, 0.6, 0.3, 0.2]
risk_budget = 0.01
result = await solve_portfolio_optimization(
assets=assets,
expected_returns=expected_returns,
risk_factors=risk_factors,
correlation_matrix=correlation_matrix,
max_allocations=max_allocations,
risk_budget=risk_budget,
description="Test portfolio optimization"
)
assert len(result) == 1
assert result[0].type == "text"
data = json.loads(result[0].text)
assert data["status"] == "optimal"
assert "weights" in data
assert "expected_return" in data
assert "portfolio_risk" in data
assert "sharpe_ratio" in data
@pytest.mark.asyncio
async def test_invalid_variable_types(self):
"""Test handling of invalid variable types."""
variables = [
{"name": "x", "type": "invalid_type"},
]
constraints = ["x == 1"]
result = await solve_constraint_satisfaction(
variables=variables,
constraints=constraints
)
assert len(result) == 1
assert result[0].type == "text"
assert "Invalid variable type" in result[0].text
@pytest.mark.asyncio
async def test_missing_required_fields(self):
"""Test handling of missing required fields."""
variables = [
{"name": "x"}, # Missing type field
]
constraints = ["x == 1"]
result = await solve_constraint_satisfaction(
variables=variables,
constraints=constraints
)
assert len(result) == 1
assert result[0].type == "text"
assert "must have 'name' and 'type' fields" in result[0].text
@pytest.mark.asyncio
async def test_invalid_objective_type(self):
"""Test handling of invalid objective types."""
variables = [{"name": "x", "shape": 1}]
objective_type = "invalid_type"
objective_expr = "x[0]"
constraints = ["x >= 0"]
result = await solve_convex_optimization(
variables=variables,
objective_type=objective_type,
objective_expr=objective_expr,
constraints=constraints
)
assert len(result) == 1
assert result[0].type == "text"
assert "Invalid objective type" in result[0].text
@pytest.mark.asyncio
async def test_invalid_sense(self):
"""Test handling of invalid optimization sense."""
sense = "invalid_sense"
objective_coeffs = [1.0]
variables = [{"name": "x1", "type": "cont"}]
constraint_matrix = []
constraint_senses = []
rhs_values = []
result = await solve_linear_programming(
sense=sense,
objective_coeffs=objective_coeffs,
variables=variables,
constraint_matrix=constraint_matrix,
constraint_senses=constraint_senses,
rhs_values=rhs_values
)
assert len(result) == 1
assert result[0].type == "text"
assert "Invalid sense" in result[0].text
@pytest.mark.asyncio
async def test_portfolio_optimization_validation(self):
"""Test portfolio optimization input validation."""
# Mismatched array lengths
assets = ["A", "B"]
expected_returns = [0.1, 0.2, 0.3] # Wrong length
risk_factors = [0.05, 0.1]
correlation_matrix = [[1.0, 0.5], [0.5, 1.0]]
result = await solve_portfolio_optimization(
assets=assets,
expected_returns=expected_returns,
risk_factors=risk_factors,
correlation_matrix=correlation_matrix
)
assert len(result) == 1
assert result[0].type == "text"
assert "length must match" in result[0].text
@pytest.mark.asyncio
async def test_error_handling(self):
"""Test general error handling."""
# This should trigger an exception in the solver
variables = [{"name": "x", "type": "integer"}]
constraints = ["invalid_syntax_here"]
result = await solve_constraint_satisfaction(
variables=variables,
constraints=constraints
)
assert len(result) == 1
assert result[0].type == "text"
# Should return an error message, not crash
assert "Error" in result[0].text