Opti-MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| solve_linear_programC | Solve a linear programming problem using Google OR-Tools. Maximizes or minimizes a linear objective function subject to linear constraints. |
| solve_integer_programC | Solve an integer programming (IP) or mixed-integer programming (MIP) problem using Google OR-Tools. Variables can be constrained to integer values. |
| solve_knapsackC | Solve the classic 0/1 knapsack problem: maximize value subject to weight capacity constraint. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 3 tools
The three tools target distinct problem classes (LP, IP/MIP, knapsack), and descriptions make the distinct semantics clear. However, knapsack is technically a special case of integer programming, and LP is a relaxation of MIP, so boundaries overlap enough that a user might wonder which solver to pick.
All tools follow a clean solve_<problem> verb_noun pattern: solve_linear_program, solve_integer_program, solve_knapsack. The convention is predictable and readable throughout.
Three tools is thin for a general optimization server; it covers only LP, IP/MIP, and a single specialized problem. The count is borderline rather than well-scoped for the apparent breadth of the domain.
Core LP and MIP solving are covered, plus one special case, giving basic coverage with no dead ends for those tasks. However, common optimization operations such as constraint programming, quadratic/nonlinear programming, assignment/transportation, and scheduling are absent, leaving notable gaps.