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SAGE — Solver Augmented Grounding Engine

Solves LP, MIP, portfolio, and scheduling problems with file input, feasibility checks, explanations, and relaxation suggestions.

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Install SAGE — Solver Augmented Grounding Engine

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "sage-solver-augmented-grounding-engine": {
      "command": "uvx",
      "args": [
        "sage-solver-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

uvx sage-solver-mcp

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 2d ago.

Overview

SAGE — Solver-Augmented Grounding Engine MCP server gives MCP-compatible agents tools for formulating, solving, and checking mathematical optimization problems. It uses HiGHS for linear and mixed-integer problems and OSQP for quadratic portfolio optimization, with Excel and CSV input and output support. Reach for it when an agent needs solver-backed feasibility, optimality, sensitivity, or infeasibility analysis instead of an approximate text answer.

Use cases

  • Solve linear programs from structured JSON
  • Optimize portfolios from Excel workbooks
  • Check infeasible schedules and identify conflicting constraints
  • Generate spreadsheet templates for optimization models
  • Explain solver results and rank constraint relaxations

Key features

  • LP and MIP solving with HiGHS
  • Quadratic portfolio optimization with OSQP
  • Excel and CSV preview and result writing
  • IIS-based infeasibility analysis
  • Solution explanations and sensitivity results
  • Constraint relaxation suggestions

Tools7

What SAGE — Solver Augmented Grounding Engine lets your agent do. Expand a tool for its parameters.

Self-reported~110 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

4/4 checks healthy over the last 45d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 11d ago via OSV.dev · sage-solver-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

Last commit7mo agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Tools exposed7Callable tools this server registers over MCP.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How SAGE — Solver Augmented Grounding Engine works

What SAGE — Solver-Augmented Grounding Engine does

SAGE — Solver-Augmented Grounding Engine exposes mathematical optimization through MCP for Claude Desktop and other MCP-compatible agents. It accepts structured JSON problem definitions or reads supported spreadsheet files, then returns solver results that can be inspected and explained.

Supported problem categories include linear programming, mixed-integer programming, portfolio optimization, and workforce scheduling. The implementation uses HiGHS for LP and MIP problems and OSQP for quadratic programming. File operations support Excel workbooks and CSV files.

The tool set covers the full workflow: creating a blank input template, previewing data, solving a problem, writing results, explaining a recent result, checking feasibility, and ranking possible constraint relaxations after an infeasible solve. Results can include objective values, variable assignments, feasibility status, dual values, reduced costs, and allowable ranges where applicable.

How it works

For a direct solve, an agent sends a JSON description containing variables, bounds, constraints, and an objective to solve_optimization. File-based workflows use read_data_file to inspect workbook or CSV contents, or solve_from_file to read input, solve the model, and write the output in one operation.

check_feasibility tests whether the stated constraints can be satisfied. If the model is infeasible, SAGE can compute an irreducible infeasible subsystem (IIS), helping identify constraints that conflict. suggest_relaxations then ranks possible changes for the most recent infeasible result. explain_solution turns the latest solve result into a plain-language account, including relevant sensitivity information when available.

This division keeps language interpretation with the MCP client while delegating feasibility and optimization calculations to dedicated solvers. The repository describes the architecture as hybrid: the language model handles ambiguity and the solver handles mathematical validation.

Setup and configuration

The documented development installation clones the repository and installs the sage-solver-core and sage-solver-mcp packages in editable mode.

Claude Desktop can launch SAGE through uvx with a server entry whose command is uvx and whose argument is sage-solver-mcp. After adding the entry to the Claude Desktop configuration, restart the application to expose the tools. If uvx is not on the application PATH, the configuration can use its full filesystem path. The README identifies separate configuration locations for macOS and Windows.

SAGE — Solver-Augmented Grounding Engine is described as local software and does not list required environment variables. Example prompts cover a direct staffing LP and a portfolio workbook.

Tools and capabilities

  • solve_optimization: solve LP, MIP, portfolio, or scheduling models from JSON.
  • read_data_file: inspect an Excel or CSV file and return a preview.
  • solve_from_file: read model data, solve it, and write results.
  • explain_solution: narrate the most recent solution.
  • check_feasibility: test feasibility and compute an IIS for infeasible models.
  • generate_template: create a blank Excel template for a supported problem type.
  • suggest_relaxations: rank constraint changes for the last infeasible result.

Limitations and notes

The repository labels SAGE — Solver-Augmented Grounding Engine version 0.1.3 as alpha.

The examples show local file paths and workbook-based workflows, so file access depends on the environment where the MCP process runs. Treat those capabilities as architectural claims rather than documented tool interfaces.

Read the full README View source on GitHub

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Frequently Asked Questions about SAGE — Solver Augmented Grounding Engine

SAGE — Solver-Augmented Grounding Engine is an MCP server that connects MCP-compatible agents to HiGHS and OSQP optimization solvers. Its main tools solve LP, MIP, portfolio, and scheduling models, read and write Excel or CSV data, check feasibility, compute IIS results, explain solutions, and suggest constraint relaxations.

Install it from source with editable installs for the core and MCP packages, or use the documented PyPI command when the package is published. Claude Desktop can launch it with `uvx sage-solver-mcp`; restart Claude Desktop after adding that server entry.

Yes, SAGE — Solver-Augmented Grounding Engine has documented Claude Desktop configuration. Add an `mcpServers` entry that runs `uvx` with `sage-solver-mcp` as its argument, using the full path to `uvx` if Claude Desktop cannot find it.

Yes, its `check_feasibility` tool checks the model and can compute an irreducible infeasible subsystem when constraints conflict. The `suggest_relaxations` tool can then rank possible constraint changes for the last infeasible result.

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