Algernon MCP
PYPI · ALGERNON-MCP · SCANNED AUG 15
Fleet orchestration for AI agents: fan tasks out to cheap parallel workers on your own LLM key.
Available components
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. How we score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs setuptools.build_meta at install time, a recognised native-build step with no shell scripting around it. View diagnostics → Pass
- 1 of 30 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (Apache-2.0).Pass
- Actively maintained (last published 8 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability77
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 344 tokens (~114/item across 3 items; 3 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management27
- Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
pypi · algernon-mcp
claude mcp add sammyboi81-algernon -- uvx algernon-mcp
codex mcp add sammyboi81-algernon -- uvx algernon-mcp
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"sammyboi81-algernon": {
"type": "local",
"command": [
"uvx",
"algernon-mcp"
],
"enabled": true
}
}
} openclaw mcp add sammyboi81-algernon --command uvx --arg algernon-mcp
mcp_servers:
sammyboi81-algernon:
command: "uvx"
args: ["algernon-mcp"] {
"mcpServers": {
"sammyboi81-algernon": {
"command": "uvx",
"args": [
"algernon-mcp"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 15 Aug 26 +4
- Stability: unverified → 0.27 ▲ functional
- 11 Aug 26 +2
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 7 Aug 26 67
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 15 Aug 2026 · Analysed pypi/algernon-mcp@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Dependencies 30 packages
| Packages resolved | 30 |
|---|---|
| Stale | 1 |
| Tree resolution | Complete |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.
algernon_dispatch ~107
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.
| Name | Type | Req | Description |
|---|---|---|---|
| max_parallel | integer | – | how many workers run at once |
| model | string | – | optional worker model override |
| tasks_json | string | yes | JSON array of {"id": str, "prompt": str} tasks |
No output schema declared.
No examples provided.
algernon_orchestrate ~118
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.
| Name | Type | Req | Description |
|---|---|---|---|
| goal | string | yes | 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 |
No output schema declared.
No examples provided.
algernon_plan ~119
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.
| Name | Type | Req | Description |
|---|---|---|---|
| goal | string | yes | 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) |
No output schema declared.
No examples provided.