Constrained Optimization
This constrained optimization MCP server by Rajnish Sharma provides AI agents with unified access to multiple optimization solvers including Z3 for constraint satisfaction, CVXPY for convex optimization, HiGHS for linear programming, and OR-Tools for combinatorial problems. Built with Python and featuring a modular architecture with typed problem definitions, it offers tools for portfolio optimization with Markowitz theory and risk constraints, production planning and resource allocation, scheduling problems like job shop and nurse scheduling, and classic combinatorial puzzles including N-Queens and knapsack variants. The implementation includes comprehensive examples with mathematical formulations, visualization capabilities through matplotlib and seaborn, and Docker deployment support, making it valuable for financial analysts optimizing investment portfolios, operations researchers solving supply chain problems, and developers building AI-powered decision support systems that require mathematical optimization capabilities.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- cloud-infra
- Source
- PulseMCP
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Constrained Optimization from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.