{
  "abstract": "We introduce ColPackAgent, an agent framework that autonomously runs Monte Carlo simulations of colloidal packing through a Model Context Protocol (MCP) tool server and an agent skill, whether as a standalone agent or inside an existing agent system. By harnessing the MCP server and agent skill, ColPackAgent executes a structured workflow for colloidal packing simulations, which are central to studies of phase behavior, self-assembly, and materials design. Without dedicated simulation tools and workflow instructions, general-purpose Large Language Model (LLM) agents tend to describe such workflows rather than execute them reliably. The MCP server exposes a custom-built colpack Python package that wraps HOOMD-blue hard-particle Monte Carlo, and the skill encodes a four-stage workflow contract. ColPackAgent can carry out the workflow interactively with human feedback, autonomously from an end-to-end prompt, or as autoresearch following a provided program file. We demonstrate the system in different modes with several colloidal packing simulation examples such as cube particles in 3D, a binary system of disks and capsules in 2D, and the 2D hard-disk freezing transition using autoresearch. We also compare model performance on this workflow across a panel of LLMs with 17 stage-specific prompts. This benchmark provides a stage-level check of how reliably different models follow the setup, planning, and analysis workflow. Together, these results show that pairing a domain Python package with MCP tools and a portable agent skill provides a practical route for turning a simulation toolkit into an agent-assisted research workflow.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.15625",
  "author_total": 2,
  "authors": [
    {
      "name": "Lijie Ding"
    },
    {
      "name": "Changwoo Do"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/39w2ng5dv9ger.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.15625",
  "fulltext_url": null,
  "id": "39w2ng5dv9ger",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.15625",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cond-mat.soft"
    ],
    [
      "arxiv",
      "cs.AI"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2605.15625",
  "pmcid": null,
  "pmid": null,
  "published": "2026-05-15",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/39w2ng5dv9ger.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:45Z",
      "native_id": "2605.15625",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.15625"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:cond-mat.soft",
      "subfield": "condensed-matter"
    }
  ],
  "title": "ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing",
  "title_text": "ColPackAgent: Agent-Skill-Guided Hard-Particle Monte Carlo Workflows for Colloidal Packing",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:45Z",
  "url": "https://research.draconitic.com/paper/39w2ng5dv9ger",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1039/d6dd00285d",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}