# Algernon MCP (pypi · algernon-mcp)

Fleet orchestration for AI agents: fan tasks out to cheap parallel workers on your own LLM key.

- Trust score: 73/100 (medium)
- Change this week: +6
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-15

## Components

- pypi · `algernon-mcp`: 73/100 (this document), [markdown](https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp.md), [page](https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp)

## Channel facts

- Registry: `pypi`
- Package: `algernon-mcp`
- Version: `0.1.0`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-15.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it.
  - 1 of 30 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (Apache-2.0).
  - Actively maintained (last published 8 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 77/100
  - AI-judged instruction clarity (excellent).
  - Tool/resource definitions use about 344 tokens (~114/item across 3 items; 3 tools + 0 resources), lean.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add sammyboi81-algernon -- uvx algernon-mcp
```

### Codex

```bash
codex mcp add sammyboi81-algernon -- uvx algernon-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "sammyboi81-algernon": {
      "type": "local",
      "command": [
        "uvx",
        "algernon-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add sammyboi81-algernon --command uvx --arg algernon-mcp
```

### Hermes

```yaml
mcp_servers:
  sammyboi81-algernon:
    command: "uvx"
    args: ["algernon-mcp"]
```

### Other

```json
{
  "mcpServers": {
    "sammyboi81-algernon": {
      "command": "uvx",
      "args": [
        "algernon-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-15 (score 73, +4)

- [functional improvement] Stability: unverified → 0.27

### 2026-08-11 (score 69, +2)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-08-07 (score 67)

First indexed and scored.

## MCP tools (3)

### `algernon_plan` (~119 tokens)

Decompose a goal into k tightly-scoped, INDEPENDENT sub-task prompts (one cheap LLM call). Tight scoping is the token lever: each worker sees only its slice, so the fleet spends fewer tokens than one bloated serial prompt. Returns a task list you can feed straight into algernon_dispatch.

Input parameters:

- `goal` (string, required): what you want accomplished
- `k` (integer): how many parallel sub-tasks to split into
- `model` (string): optional worker model override (defaults to the cheap tier)

### `algernon_dispatch` (~107 tokens)

Run N tightly-scoped tasks CONCURRENTLY on a fleet of cheap workers and collect every result. Stay free to think while the fleet works — N tight tasks in parallel beat one bloated serial prompt. Each worker runs on YOUR LLM key.

Input parameters:

- `max_parallel` (integer): how many workers run at once
- `model` (string): optional worker model override
- `tasks_json` (string, required): JSON array of {"id": str, "prompt": str} tasks

### `algernon_orchestrate` (~118 tokens)

One shot: plan THEN dispatch. Hand it a goal; it splits the goal into k tight sub-tasks and fans them out across the fleet, then returns the plan and all results. Orchestrate a fleet, spend fewer tokens — and stay free to think.

Input parameters:

- `goal` (string, required): what you want accomplished
- `k` (integer): how many parallel sub-tasks to split into
- `max_parallel` (integer): how many workers run at once
- `model` (string): optional worker model override

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp#diagnostics

## Score history

- 2026-08-15: 73
- 2026-08-14: 69
- 2026-08-13: 69
- 2026-08-12: 69
- 2026-08-11: 69
- 2026-08-10: 67
- 2026-08-09: 67
- 2026-08-08: 67
- 2026-08-07: 67

## Links

- PyPI project: https://pypi.org/project/algernon-mcp/
- Socket report: https://socket.dev/pypi/package/algernon-mcp
- Repository: https://github.com/sammyboi81/algernon
- Changelog RSS feed: https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp.json
- HTML version of this page: https://verifymcp.io/servers/sammyboi81-algernon/algernon-mcp