How should opti-mcp be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this mcp.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

opti-mcp answer-first brief

MCP server with Google OR-Tools for solving Mixed Integer Programming problems Opti-MCP: Optimization MCP Server A Model Context Protocol (MCP) server that provides tools for solving optimization problems using Google OR-Tools. This server enables AI assistants to solve Linear Programming (LP), Integer Programming (IP), Mixed-Integer Programming (MIP), and classic combinatorial optimization problems. Features - **Linear Programming**: Solve continuous optimization problems with linear constrain Published capability contract available. No trust telemetry is available yet. Last updated 2/24/2026.

Freshness

Last checked 2/22/2026

Best For

Contract is available with explicit auth and schema references.

Not Ideal For

opti-mcp is not ideal for teams that need stronger public trust telemetry, lower setup complexity, or more explicit contract coverage before production rollout.

Evidence Sources Checked

editorial-content, capability-contract, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Claim this agent

Agent DossierGitHubSafety: 80/100

opti-mcp

MCPverified

Public facts

Change events

Artifacts

Freshness

Feb 22, 2026

Verifiededitorial-content1 verified compatibility signal

Published capability contract available. No trust telemetry is available yet. Last updated 2/24/2026.

Schema refs publishedTrust evidence available

Trust score

Unknown

Compatibility

MCP

Freshness

Feb 22, 2026

Vendor

Average Joe25

Artifacts

Benchmarks

Last release

1.0.0

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Published capability contract available. No trust telemetry is available yet. Last updated 2/24/2026.

View Source

Setup snapshot

git clone https://github.com/average-joe25/opti-mcp.git

1
Setup complexity is MEDIUM. Standard integration tests and API key provisioning are required before connecting this to production workloads.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Average Joe25

profilemedium

Observed Feb 24, 2026Source link Provenance

Compatibility (2)

Protocol compatibility

MCP

contracthigh

Observed Feb 24, 2026Source link Provenance

Auth modes

mcp, api_key

contracthigh

Observed Feb 24, 2026Source link Provenance

Artifact (1)

Machine-readable schemas

OpenAPI or schema references published

contracthigh

Observed Feb 24, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB MCP

Extracted files

Examples

Snippets

Languages

typescript

Executable Examples

bash

git clone <your-repo-url>
cd opti-mcp

bash

npm install

bash

pip install ortools

bash

npm run build

json

{
  "mcpServers": {
    "opti-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/opti-mcp/dist/index.js"]
    }
  }
}

json

{
  "objective": {
    "coefficients": [3, 5],
    "maximize": true
  },
  "constraints": [
    {
      "coefficients": [1, 0],
      "upper_bound": 4
    },
    {
      "coefficients": [0, 2],
      "upper_bound": 12
    },
    {
      "coefficients": [3, 2],
      "upper_bound": 18
    }
  ],
  "variable_bounds": [[0, "Infinity"], [0, "Infinity"]]
}

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB MCP

Docs source

GITHUB MCP

Editorial quality

ready

Full README

Opti-MCP: Optimization MCP Server

A Model Context Protocol (MCP) server that provides tools for solving optimization problems using Google OR-Tools. This server enables AI assistants to solve Linear Programming (LP), Integer Programming (IP), Mixed-Integer Programming (MIP), and classic combinatorial optimization problems.

Features

Linear Programming: Solve continuous optimization problems with linear constraints
Integer/Mixed-Integer Programming: Solve optimization problems with integer decision variables
Knapsack Problem: Solve the classic 0/1 knapsack problem efficiently
Built on Google OR-Tools for robust, production-grade optimization
Simple JSON-based interface

Prerequisites

Node.js 18 or higher
Python 3.8 or higher
Google OR-Tools Python library

Installation

Clone this repository:

git clone <your-repo-url>
cd opti-mcp

Install Node.js dependencies:

npm install

Install Python dependencies:

pip install ortools

Build the TypeScript code:

npm run build

Configuration

Add to your MCP settings file (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "opti-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/opti-mcp/dist/index.js"]
    }
  }
}

Available Tools

1. solve_linear_program

Solves linear programming problems where all variables are continuous.

Example: Production Planning

{
  "objective": {
    "coefficients": [3, 5],
    "maximize": true
  },
  "constraints": [
    {
      "coefficients": [1, 0],
      "upper_bound": 4
    },
    {
      "coefficients": [0, 2],
      "upper_bound": 12
    },
    {
      "coefficients": [3, 2],
      "upper_bound": 18
    }
  ],
  "variable_bounds": [[0, "Infinity"], [0, "Infinity"]]
}

This maximizes 3x₀ + 5x₁ subject to:

x₀ ≤ 4
2x₁ ≤ 12
3x₀ + 2x₁ ≤ 18
x₀, x₁ ≥ 0

2. solve_integer_program

Solves integer or mixed-integer programming problems.

Example: Assignment Problem

{
  "objective": {
    "coefficients": [1, 2, 3, 4],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [1, 1, 0, 0],
      "lower_bound": 1,
      "upper_bound": 1
    },
    {
      "coefficients": [0, 0, 1, 1],
      "lower_bound": 1,
      "upper_bound": 1
    }
  ],
  "variable_bounds": [[0, 1], [0, 1], [0, 1], [0, 1]],
  "integer_variables": [0, 1, 2, 3]
}

This solves an assignment problem where variables must be binary (0 or 1).

3. solve_knapsack

Solves the 0/1 knapsack problem.

Example: Item Selection

{
  "values": [360, 83, 59, 130, 431, 67, 230, 52, 93, 125],
  "weights": [7, 0, 30, 22, 80, 94, 11, 81, 70, 64],
  "capacity": 850
}

Maximizes total value while keeping total weight ≤ capacity.

Example Problems

Diet Problem

Minimize cost while meeting nutritional requirements:

{
  "objective": {
    "coefficients": [2.5, 1.8, 3.0, 0.5],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [10, 5, 8, 2],
      "lower_bound": 50
    },
    {
      "coefficients": [3, 8, 1, 6],
      "lower_bound": 30
    },
    {
      "coefficients": [5, 4, 7, 3],
      "lower_bound": 40
    }
  ]
}

Bin Packing

Pack items into minimum number of bins:

{
  "objective": {
    "coefficients": [1, 1, 1],
    "maximize": false
  },
  "constraints": [
    {
      "coefficients": [5, 0, 0],
      "upper_bound": 10
    },
    {
      "coefficients": [0, 7, 0],
      "upper_bound": 10
    },
    {
      "coefficients": [0, 0, 4],
      "upper_bound": 10
    }
  ],
  "integer_variables": [0, 1, 2]
}

Response Format

All solvers return a JSON response with:

{
  "status": "OPTIMAL",
  "objective_value": 34.0,
  "solution": [2.0, 6.0],
  "solve_time_ms": 15
}

status: OPTIMAL, INFEASIBLE, UNBOUNDED, or UNKNOWN
objective_value: The optimal value found (null if not optimal)
solution: Array of variable values (null if not optimal)
solve_time_ms: Time taken to solve in milliseconds

For knapsack problems:

{
  "status": "OPTIMAL",
  "total_value": 1030,
  "total_weight": 850,
  "selected_items": [0, 2, 3, 4],
  "capacity": 850
}

Development

# Watch mode for development
npm run dev

# Build for production
npm run build

# Run the server
npm start

How It Works

The MCP server:

Receives optimization problems via MCP tool calls
Translates them into Python scripts using OR-Tools
Executes the Python scripts
Returns formatted results

Limitations

Requires Python 3.8+ with OR-Tools installed
Currently uses GLOP for LP and SCIP for MIP (requires SCIP installation for best performance)
Large problems may take significant time to solve

License

MIT

Contributing

Contributions welcome! Please open an issue or PR.

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

Verifiedcapability-contract

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

ready

Auth

mcp, api_key

Streaming

Data region

global

Protocol support

MCP: verified

Requires: mcp, lang:typescript

Forbidden: none

Guardrails

Operational confidence: medium

Contract is available with explicit auth and schema references.

Trust confidence is not low and verification freshness is acceptable.

Protocol support is explicitly confirmed in contract metadata.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract"

curl -s "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

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Machine Appendix

Contract JSON

{
  "contractStatus": "ready",
  "authModes": [
    "mcp",
    "api_key"
  ],
  "requires": [
    "mcp",
    "lang:typescript"
  ],
  "forbidden": [],
  "supportsMcp": true,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": "https://github.com/average-joe25/opti-mcp#input",
  "outputSchemaRef": "https://github.com/average-joe25/opti-mcp#output",
  "dataRegion": "global",
  "contractUpdatedAt": "2026-02-24T19:46:42.585Z",
  "sourceUpdatedAt": "2026-02-24T19:46:42.585Z",
  "freshnessSeconds": 19574162
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "MCP"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_MCP",
      "generatedAt": "2026-10-09T09:02:44.667Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "MCP",
      "type": "protocol",
      "support": "supported",
      "confidenceSource": "contract",
      "notes": "Confirmed by capability contract"
    },
    {
      "key": "mcp",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "optimization",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "or-tools",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "mip",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "linear-programming",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "integer-programming",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "cli",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:MCP|supported|contract capability:mcp|supported|profile capability:optimization|supported|profile capability:or-tools|supported|profile capability:mip|supported|profile capability:linear-programming|supported|profile capability:integer-programming|supported|profile capability:cli|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "MCP",
    "href": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "sourceType": "contract",
    "confidence": "high",
    "observedAt": "2026-02-24T19:46:42.585Z",
    "isPublic": true
  },
  {
    "factKey": "auth_modes",
    "category": "compatibility",
    "label": "Auth modes",
    "value": "mcp, api_key",
    "href": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "sourceType": "contract",
    "confidence": "high",
    "observedAt": "2026-02-24T19:46:42.585Z",
    "isPublic": true
  },
  {
    "factKey": "schema_refs",
    "category": "artifact",
    "label": "Machine-readable schemas",
    "value": "OpenAPI or schema references published",
    "href": "https://github.com/average-joe25/opti-mcp#input",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/contract",
    "sourceType": "contract",
    "confidence": "high",
    "observedAt": "2026-02-24T19:46:42.585Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Average Joe25",
    "href": "https://github.com/average-joe25/opti-mcp",
    "sourceUrl": "https://github.com/average-joe25/opti-mcp",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:43:14.176Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/mcp-average-joe25-opti-mcp/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]