Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Schema
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| solve_constraint_satisfaction | Solve constraint satisfaction problems using Z3 SMT solver.
This tool is ideal for logical reasoning, puzzle solving, and constraint satisfaction
problems where you need to find values that satisfy a set of logical constraints.
Args:
variables: List of variable definitions with 'name' and 'type' fields
constraints: List of constraint expressions as strings
description: Optional problem description
timeout: Optional timeout in milliseconds
Returns:
Solution results including variable values and satisfiability status
Example:
variables = [
{"name": "x", "type": "integer"},
{"name": "y", "type": "integer"}
]
constraints = [
"x + y == 10",
"x - y == 2"
] |
| solve_convex_optimization | Solve convex optimization problems using CVXPY.
This tool is ideal for mathematical optimization problems with convex objectives
and constraints, including linear programming, quadratic programming, and
semidefinite programming.
Args:
variables: List of variable definitions with 'name' and 'shape'
objective_type: Either 'minimize' or 'maximize'
objective_expr: The objective function expression as a string
constraints: List of constraint expressions as strings
parameters: Dictionary of parameter values (e.g., matrices A, b)
description: Optional problem description
Returns:
Solution results including variable values and objective value
Example:
variables = [{"name": "x", "shape": 2}]
objective_type = "minimize"
objective_expr = "cp.sum_squares(x)"
constraints = ["x >= 0", "cp.sum(x) == 1"] |
| solve_linear_programming | Solve linear and mixed-integer programming problems using HiGHS.
This tool is ideal for linear programming, mixed-integer linear programming,
and large-scale optimization problems with linear constraints.
Args:
sense: Optimization sense, either "minimize" or "maximize"
objective_coeffs: List of objective function coefficients
variables: List of variable definitions with optional bounds and types
constraint_matrix: 2D list representing the constraint matrix (dense format)
constraint_senses: List of constraint directions ("<=", ">=", "=")
rhs_values: List of right-hand side values for constraints
options: Optional solver options dictionary
description: Optional problem description
Returns:
Solution results including variable values and objective value
Example:
sense = "minimize"
objective_coeffs = [1.0, 2.0, 3.0]
variables = [
{"name": "x1", "lb": 0, "ub": 10, "type": "cont"},
{"name": "x2", "lb": 0, "ub": None, "type": "int"},
{"name": "x3", "lb": 0, "ub": 1, "type": "bin"}
]
constraint_matrix = [[1, 1, 0], [0, 1, 1]]
constraint_senses = ["<=", ">="]
rhs_values = [5, 3] |
| solve_constraint_programming | Solve constraint programming problems using OR-Tools.
This tool is ideal for combinatorial optimization problems, scheduling,
assignment problems, and constraint satisfaction with discrete variables.
Args:
variables: List of variable definitions with 'name', 'type', and optional 'domain'/'shape'
constraints: List of constraint expressions as strings
objective: Optional objective definition with 'type' and 'expression'
parameters: Dictionary of solver parameters
description: Optional problem description
Returns:
Solution results including variable values and feasibility status
Example:
variables = [
{"name": "x", "type": "integer", "domain": [0, 10]},
{"name": "y", "type": "boolean"}
]
constraints = [
"x + y >= 5",
"x - y <= 3"
]
objective = {"type": "minimize", "expression": "x + y"} |
| solve_portfolio_optimization | Solve portfolio optimization problems using modern portfolio theory.
This tool implements Markowitz mean-variance optimization to find optimal
asset allocations that maximize expected return while constraining risk.
Args:
assets: List of asset names
expected_returns: List of expected returns for each asset
risk_factors: List of risk factors (standard deviations) for each asset
correlation_matrix: Correlation matrix between assets
max_allocations: Optional maximum allocation limits for each asset
risk_budget: Optional maximum portfolio risk (variance)
description: Optional problem description
Returns:
Optimal portfolio weights and performance metrics
Example:
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], ...]
max_allocations = [0.4, 0.6, 0.3, 0.2]
risk_budget = 0.01 |