Constrained Opt MCP Server
constrained-opt-mcp
PyPI
v1.0.1
Published by an unidentified publisher — no publish provenance and no public repository, so the publisher could not be verified and the source cannot be independently located.
General Purpose MCP Server for Constrained Optimization - AI agents for optimization tasks such as portfolio optimization, scheduling, and combinatorial problems
The grade answers one question — how safe is this server for you to adopt — so it is computed in two auditable stages. Nothing below is an opinion or an LLM's guess; every line is a real term the deterministic engine applied, and the same input always yields the same number.
1. Threat score — 100 − 0 = 100. What the published surface and source actually contain:
The deterministic scan raised no scored threat in the surface it inspected — the threat score stayed at 100. Capability observations and advisory notes are recorded but never lower it.
2. Client adoption risk — 100 − 8 = 92. Three small, subtract-only factors that reflect your risk in adopting it — a clean scan proves less on a powerful, unverified or barely-inspectable package, so the grade says so plainly:
| Points | Adoption-risk factor |
|---|---|
| −6 | capability blast radius (high) — client exposure if the model is manipulated |
| −2 | publisher verification (unlinked) — no provenance/repo link, but the shipped source was fully read |
Capability observations and info notes are shown under Findings but never scored.
Open any row's finding below for the file, line and evidence behind a deduction.
Findings 7
high Dynamic evaluation of a non-literal value (constrained_opt_mcp/solvers/cvxpy_solver.py)MTC-SRC-010
In the server's implementation (`constrained_opt_mcp/solvers/cvxpy_solver.py:75`): Evaluating a runtime value as code (rather than a fixed literal) executes whatever reaches it — a direct RCE primitive, and almost never necessary in legitimate code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: l dictionary return eval(expr_str, {"__builtins__": {}}, local_dict) def solve_cvxpy_problem(problem: CVXPYProblem
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/cvxpy_solver.py
high Dynamic evaluation of a non-literal value (constrained_opt_mcp/solvers/ortools_solver.py)MTC-SRC-010
In the server's implementation (`constrained_opt_mcp/solvers/ortools_solver.py:181`): Evaluating a runtime value as code (rather than a fixed literal) executes whatever reaches it — a direct RCE primitive, and almost never necessary in legitimate code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: ocal dictionary eval(constraint.expression, {"__builtins__": {}}, local_dict) return Success(None) e
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/ortools_solver.py
high Dynamic evaluation of a non-literal value (constrained_opt_mcp/solvers/z3_solver.py)MTC-SRC-010
In the server's implementation (`constrained_opt_mcp/solvers/z3_solver.py:111`): Evaluating a runtime value as code (rather than a fixed literal) executes whatever reaches it — a direct RCE primitive, and almost never necessary in legitimate code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: z3_constraint = eval(constraint.expression, {"__builtins__": {}}, local_dict) return Success(z3_constrai
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/z3_solver.py
high Dynamic code execution in server code (constrained_opt_mcp/solvers/cvxpy_solver.py)MTC-SRC-001
In the server's implementation (`constrained_opt_mcp/solvers/cvxpy_solver.py:75`): Evaluating strings as code is the most direct RCE primitive; if any tool input reaches it, the server executes attacker-chosen code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: l dictionary return eval(expr_str, {"__builtins__": {}}, local_dict) def solve_cvxpy_problem(problem: CVXPYProblem
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/cvxpy_solver.py
high Dynamic code execution in server code (constrained_opt_mcp/solvers/ortools_solver.py)MTC-SRC-001
In the server's implementation (`constrained_opt_mcp/solvers/ortools_solver.py:181`): Evaluating strings as code is the most direct RCE primitive; if any tool input reaches it, the server executes attacker-chosen code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: ocal dictionary eval(constraint.expression, {"__builtins__": {}}, local_dict) return Success(None) e
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/ortools_solver.py
high Dynamic code execution in server code (constrained_opt_mcp/solvers/z3_solver.py)MTC-SRC-001
In the server's implementation (`constrained_opt_mcp/solvers/z3_solver.py:111`): Evaluating strings as code is the most direct RCE primitive; if any tool input reaches it, the server executes attacker-chosen code. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: z3_constraint = eval(constraint.expression, {"__builtins__": {}}, local_dict) return Success(z3_constrai
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server constrained_opt_mcp/solvers/z3_solver.py
low Shell/command execution in packaging/dev tooling (scripts/build_and_test.py)MTC-SRC-002
In a packaging/dev/install script (shipped, but not the server runtime) (`scripts/build_and_test.py:15`): Spawning a shell/process is command-execution capability; with unsanitized tool input it is command injection / RCE. This is read from the code itself — not from the tool description — so a poisoned server cannot hide it behind honest-looking metadata.
Evidence: try: result = subprocess.run(cmd, shell=True, check=True, capture_output=True, text=True) print(f"✅ {d
Fix: Review this call path: confirm it never receives unsanitized tool input, constrain it, or remove it. Treat a server whose code reaches these sinks as high-capability regardless of what its tools claim.
Location: server scripts/build_and_test.py
Tools 5
Each tool and what it can reach — statically extracted from the published source.
solve_constraint_programmingno sensitive capabilitysolve_constraint_satisfactionno sensitive capabilitysolve_convex_optimizationno sensitive capabilitysolve_linear_programmingno sensitive capabilitysolve_portfolio_optimizationno sensitive capability
What this scan could not see
- Tools were statically extracted from the published source (5 recovered), not enumerated from a running server. Tool-poisoning, Unicode-smuggling, capability and toxic-flow analysis ran on this inferred surface, but a mis-parsed registration could be missed or mis-attributed, so tool-derived findings are capped below “confirmed”. To grade the real runtime surface, scan the running server: --command "npx -y <package>".
Versions 1
Scan history per published version. The engine is deterministic — the same version always yields the same score, so a changed score means the package itself changed.
| Version | Score | Findings | Engine | Scanned |
|---|---|---|---|---|
v1.0.1 latest |
A 92/100 | 7 | 1.13.0 | 2026-08-25 |
Embed this score
Show this server's live Trust Score in your README, docs or website. The badge is served straight from the registry and updates automatically after every rescan — no API key needed. It links back to this page, so anyone who sees the grade can also read the findings behind it instead of taking a number on faith.
Verify this score yourself
The score above is reproducible: the same package version always yields the same result. Run it locally or over the free API — no account, no LLM, fully deterministic.
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