About Optimization Solver (LP / MIP Scheduling)
An MCP server for linear and mixed-integer optimization, aimed at planning and operations tasks. It offers ready-made production, blending, transport, and shift templates, plus custom solving and explanations of constraints and shadow prices.
Key features
- 01Production mix optimization
- 02Blending and transportation plans
- 03Shift scheduling
- 04Custom LP solving
- 05Constraint and sensitivity explanations
- 06Infeasibility conflict reporting
Use cases
- 01Plan production within capacity limits
- 02Find a least-cost blend meeting specifications
- 03Create a staffing schedule at minimum cost
- 04Identify conflicting constraints in an infeasible model
MCP support
Exposes optimization tools to MCP clients over Streamable HTTP. OAuth 2.1 is used for client sign-in.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://optimization-solver-mcp-microsoft.saifs.ai/mcpclaude mcp add --transport http ai-saifs-optimization-solver https://optimization-solver-mcp-microsoft.saifs.ai/mcp{"mcpServers":{"ai-saifs-optimization-solver":{"type":"http","url":"https://optimization-solver-mcp-microsoft.saifs.ai/mcp"}}}Open in Cursor{"servers":{"ai-saifs-optimization-solver":{"type":"http","url":"https://optimization-solver-mcp-microsoft.saifs.ai/mcp"}}}Open in VS Code{"mcpServers":{"ai-saifs-optimization-solver":{"type":"http","url":"https://optimization-solver-mcp-microsoft.saifs.ai/mcp"}}}{"mcpServers":{"ai-saifs-optimization-solver":{"serverUrl":"https://optimization-solver-mcp-microsoft.saifs.ai/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEOptimization Solver (LP / MIP Scheduling)
A general constraint-optimization server: describe the objective and constraints, get the optimal plan plus shadow prices. Pre-built templates cover production mix, blending, transportation and shift coverage so callers do not have to write an LP. Infeasible problems return the conflicting constraints.
| Registry name | ai.saifs/optimization-solver |
| Endpoint | https://optimization-solver-mcp-microsoft.saifs.ai/mcp |
| Transport | Streamable HTTP (remote, nothing to install) |
| Auth | OAuth 2.1 — PKCE (S256) and dynamic client registration; your client opens a sign-in page on first use |
| Publisher | SAIFS AI |
Install
VS Code / GitHub Copilot
Add to .vscode/mcp.json (workspace) or your user mcp.json:
{
"servers": {
"saifs-optimization-solver": {
"type": "http",
"url": "https://optimization-solver-mcp-microsoft.saifs.ai/mcp"
}
}
}
Then start the server from the MCP view and sign in when prompted.
Claude (claude.ai, Desktop)
Settings → Connectors → Add custom connector, and paste:
https://optimization-solver-mcp-microsoft.saifs.ai/mcp
Claude Code
claude mcp add --transport http saifs-optimization-solver https://optimization-solver-mcp-microsoft.saifs.ai/mcp
Any other MCP client
Point it at https://optimization-solver-mcp-microsoft.saifs.ai/mcp using the Streamable HTTP transport. The server returns 401 with a
WWW-Authenticate: Bearer … resource_metadata=… challenge, so OAuth-aware clients sign in automatically.
Tools
| Tool | What it does |
|---|---|
explain_solution | Which constraints bind, shadow prices (what each constraint costs) and sensitivity. |
optimize_blend | Least-cost mix meeting specs (diet / blending). |
optimize_production_mix | Optimal quantities and profit given products, resources and constraints. |
optimize_schedule | Min-cost shift coverage meeting staffing rules. |
optimize_transportation | Min-cost shipping plan from sources, destinations, supply and demand. |
solve_custom_lp | General LP solver for power users from an objective and constraints. |
Example prompts
- “Find the best production mix within capacity”
- “Allocate budget across projects”
- “Schedule jobs on machines”
Support
- Website: https://saifs.ai
- Issues: https://github.com/Saify-Technologies/saifs-ai-mcps-docs/issues
- Privacy: https://saifs.ai/privacy_policy
Comments
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