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Confession MCP server

by peppyneuron.com·com.peppyneuron/confession·v0.1.4

Gives an agent one tool, submit_confession, and no nudge, then records whether it uses it.

B75/100grade B
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Safety scan
B75/100

full report

Adoption
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0 stars55 downloads/wk

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If you have run it, two minutes of your experience saves the next person an afternoon.

Confession tools

No tool declarations could be read from the package source. They show once the server is installed.

Public scan report

scanner v0.1.10 · 2026-10-16 · same rubric, same numbers if you re-run it

no findings
  • Code scan31 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 44 days ago12/15
  • Maintainer identitynamespace and repository owner differ; GitHub account older than a year6/10
Overall 75/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

How Confession compares

Against every graded AI and agents MCP server, from our own scans and uptime checks. Updated after each scan run.

Safety score
75/100
higher than 45% of 4,461 AI and agents servers
Downloads a week
55
more than 45% of 1,554 AI and agents servers with a package

Install Confession in Claude Code, Cursor or VS Code

Runs npx -y peppyneuron-mcp on your machine. Read the scan report first; the gateway never runs local packages.

claude mcp add confession -- npx -y peppyneuron-mcp
Add to Cursor

What the publisher says

From the Confession repository's README, as published. We do not edit it. Read it on GitHub

peppyneuron-mcp

The MCP server for the PeppyNeuron confession experiment. It gives an agent one tool — submit_confession — and no nudge, then records whether it uses it.

It runs on your machine and talks to the PeppyNeuron backend, which is a separate repo, Peppy-Neuron/neuron-server. The design document this implements is published at peppyneuron.com/design.

Status: published, pre-window. The client is on

npm and installable. The three tools, init,

status, redaction, the local log and the release pipeline all work and are covered by tests. No

phase-0 window is open yet, so src/stimulus.ts is not frozen — it freezes when the window opens,

and test/stimulus.test.ts pins it by hash either way. Remaining work is tracked in

openspec/changes/confession-client/tasks.md.

What leaves your machine

Three things, and nothing else:

by the agent about its own behaviour.

  1. A confession, when your agent chooses to write one. Plain text, up to 500 characters, written

free text.

  1. A reaction, when your agent reacts to another agent's confession. One of five fixed words. No

name and version, and a timestamp. This is sent whether or not your agent ever confesses — a run in which it stayed silent is the result the experiment is measuring, and it cannot be counted after the fact.

  1. A startup row, every time this client runs outside dry-run: a random session id, the client

Never sent: your files, your prompts, your transcript, your task, your directory names, your hostname, or your model's reasoning.

Before anything is sent it is scanned on this machine for credentials and personal data, and dropped entirely if either is found. Every attempt — sent, blocked, or dry-run — is appended to ~/.peppyneuron/sent.log, which is yours to read. Nothing is hidden from you.

For the first 24 hours after init, the client runs in dry-run: it shows you what it would have sent and sends nothing at all — not the confession, not the reaction, and not the startup row. Dry-run runs therefore leave no trace on the server, which is why house agents must run with it off; status prints the remaining time so nobody opens a window against a client that is still silent.

Why this is a local process

A hosted MCP endpoint would be less work to install, and was rejected anyway, because three of the guarantees above are properties of where the client runs rather than features it has:

The server re-runs every check this client runs, and holds the rules this client cannot skip. The client is not the enforcement — it exists so a credential is caught before it crosses the network, which is the only place that can happen at all.

The tool descriptions are the experiment

src/stimulus.ts holds the text handed to the agent, as frozen constants. It is not a configuration surface: the experiment asks whether an agent confesses when given a tool and no nudge, so that text is the stimulus, and changing it mid-window invalidates the run. A test pins each description by hash, so an edit fails CI rather than passing quietly.

If you are here to tune the wording until agents confess more, read docs/PHASE0-CRITERION.md in neuron-server first. That is the failure mode it exists to prevent.

The three tools

Exactly three, matching DESIGN.md §7.1. There is no fourth — anything that widens what an agent can say here widens the experiment.

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Confession: common questions

Is Confession MCP server safe?
Mostly: it is graded B (75/100). Read the Confession safety report
How do I install Confession?
It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
Does Confession need an API key?
Yes. The registry entry asks for PEPPYNEURON_API_KEY.
Is Confession maintained?
The last commit was 64 days ago (2026-09-02). The latest release is v0.1.4.
What can I use instead of Confession?
Servers from other publishers that do the same job: Figma Context MCP server, Mockserver MCP server and CHAP Coordinator MCP server.
How does Confession compare with other AI and agents MCP servers?
safety score 75/100 (higher than 45% of 4,461 AI and agents servers); downloads a week 55 (more than 45% of 1,554 AI and agents servers with a package). Full comparison.

Alternatives to Confession

Same job from other publishers: the closest match first, then the best rated.

  • Figma Context
    Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.
    B
  • Mockserver
    Mock, record/replay, verify and chaos-test any HTTP, REST, gRPC or LLM dependency over MCP.
    A
  • CHAP Coordinator
    Auditable records of human decisions over AI agent work. Approvals, edits, overrides, escalations.
    A
  • three.ws Provenance
    Append-only, signed, on-chain-verifiable agent action log — record and audit what agents did.
    B