Algernon MCP
PYPI · ALGERNON-MCP · SCANNED SEP 30
Fan a goal out to cheap parallel LLM workers on your own key or a free local Ollama. Keep your mind.
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 → Why this is hard to 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 build step with no custom scripting around it. View diagnostics → Pass
- 0 of 28 dependencies flagged as unhealthy. View diagnostics → Pass
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 24 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability75
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 461 tokens (~92/item across 5 items; 5 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management90
- Stability observed for 27 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage97
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 91% of tool parameters carry a description.Partial
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 5 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the Algernon MCP server?
Algernon MCP runs locally as a PyPI package, launched with uvx algernon-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · algernon-mcp
claude mcp add sammyboi81-algernon -- uvx algernon-mcp
{
"mcpServers": {
"sammyboi81-algernon": {
"command": "uvx",
"args": [
"algernon-mcp"
]
}
}
} {
"servers": {
"sammyboi81-algernon": {
"command": "uvx",
"args": [
"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": {
"Transport": "stdio",
"Command": "uvx",
"Arguments": [
"algernon-mcp"
]
}
}
} assistant mcp add sammyboi81-algernon -t stdio -c uvx -a 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.
- 30 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.
- 28 Sept 26 +1
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 27 Sept 26 −3
- Stability: pass → 0.80 functional
- 26 Sept 26 0
- Stability: 0.97 → pass security
- 25 Sept 26 +1
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 23 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 87 to 90. That category is still filling its 30-day observation window: 26 days of observed history at the previous scan, 27 at this one. The score rises as the window fills, whether or not the server changes.
- 21 Sept 26 −2
- Stability: pass → 0.83 functional
- 20 Sept 26 0
- Stability: 0.97 → pass security
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 30 Sept 2026 · Analysed pypi/algernon-mcp@0.1.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | setuptools.build_meta |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
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. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
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_doctor ~53
Which provider/model the fleet will run on right now and why (Anthropic key, OpenAI-compatible key, or a free local Ollama auto-detected). Call this first if a dispatch returns an error.
Input schema present but exposes no named parameters.
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.
algernon_upgrade ~64
What Algernon v2 (paid) adds — budgets, cache, {{id}} data flow, progress + background jobs — and, if you give your email, a free 14-day v2 trial key. Opt-in only.
| Name | Type | Req | Description |
|---|---|---|---|
| string | – | – |
No output schema declared.
No examples provided.
What is the Algernon MCP server?
Algernon MCP is listed in the public MCP registry as io.github.sammyboi81/algernon. Fan a goal out to cheap parallel LLM workers on your own key or a free local Ollama. Keep your mind. This page covers its PyPI package (algernon-mcp).
Is the Algernon MCP server safe to use?
Algernon MCP scores 83 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 30 September 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Algernon MCP server expose?
Algernon MCP exposes 5 tools: algernon_doctor, algernon_upgrade, algernon_plan, algernon_dispatch, algernon_orchestrate. Their descriptions and schemas cost roughly 461 tokens of context every time the server is loaded.
Is the Algernon MCP server still maintained?
Algernon MCP is still listed as active in the MCP registry. We last reached this channel on 30 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Algernon MCP server under?
Algernon MCP declares the Apache-2.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.