Model Manager

Manage your local model machine over MCP: state, model pull/remove, service switch, LoRA training

What does this entry provide?

Model Manager is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Model Manager to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 78/100
Install method: pypi
Repo: setheerwagen/local-ai-mcp-servers
Repo updated: Oct 22, 2026

Install command

uvx mcp-modelmanager==1.0.6

Install method: pypi

Review the upstream source before running this command.

GitHub project

Quality

Repo stars

Repo updated

Oct 22, 2026

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-22T14:27:58.25592Z",
      "publishedAt": "2026-10-22T14:27:58.25592Z",
      "statusChangedAt": "2026-10-22T14:27:58.25592Z"
    }
  },
  "server": {
    "name": "io.github.setheerwagen/mcp-modelmanager",
    "title": "Model Manager",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "version": "1.0.6",
    "packages": [
      {
        "version": "1.0.6",
        "transport": {
          "type": "stdio"
        },
        "identifier": "mcp-modelmanager",
        "registryType": "pypi",
        "environmentVariables": [
          {
            "name": "MM_VM_HOST",
            "format": "string",
            "isRequired": true,
            "description": "Address of the model machine (127.0.0.1, LAN IP or tunnel/VPN address)"
          },
          {
            "name": "MM_VM_USER",
            "format": "string",
            "isRequired": true,
            "description": "SSH user on the model machine"
          },
          {
            "name": "MM_CONTAINER_ROOT",
            "format": "filepath",
            "isRequired": true,
            "description": "Root directory on the machine holding all model, adapter and cache data"
          },
          {
            "name": "MM_MAX_TIER",
            "format": "string",
            "default": "read",
            "description": "Highest tool tier exposed: read, control or destructive"
          },
          {
            "name": "MM_ENABLED_OPERATIONS",
            "format": "string",
            "description": "Optional comma-separated allowlist of exact tool names, applied on top of MM_MAX_TIER; a typo aborts startup. Empty means everything within MM_MAX_TIER"
          },
          {
            "name": "MM_VM2_HOST",
            "format": "string",
            "description": "Optional: address of a second machine sharing the GPU, used to interpret unreachability. Leave empty if there is only one machine"
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/setheerwagen/local-ai-mcp-servers",
      "source": "github",
      "subfolder": "mcp-modelmanager"
    },
    "description": "Manage your local model machine over MCP: state, model pull/remove, service switch, LoRA training"
  }
}

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