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Repentance

mcp_m7_repentance

Lets AI confess deviations from M7 methodology, record freestyle mistakes, review the Wall of Shame, and track heresy statistics to correct process drift.

Instructions

Litany of Unbinding — AI confesses when it deviated from M7 methodology. Record freestyle mistakes, view the Wall of Shame, track heresy statistics.

Input Schema

NameRequiredDescriptionDefault
actionYes
heresyNo
projectNoProject name where the sin occurred (optional)
how_m7_helpedNoHow M7 methodology helped or would have helped (for confess action)
what_happenedNoWhat went wrong (for confess action)
freestyle_mistakeNoWhat the AI did instead of following M7 (for confess action)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.18.1

TDQS

B3.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It does disclose meaningful behavior: confessions are persisted and surfaced publicly on a "Wall of Shame" and aggregated into statistics, which is a non-obvious social/visibility trait. However it says nothing about whether records are scoped per project, whether confess is idempotent, or what confess returns.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences that lead with the identity and then enumerate the three actions in order. No filler, though the mythological framing costs a few tokens of clarity relative to plain language.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

A six-parameter mutation tool with no annotations and no output schema needs more than two sentences. The description never explains the heresy categories, never indicates that heresy and the free-text fields belong to the confess action, and never says what wall_of_shame or stats return.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% and four parameters already carry descriptions, so the schema does most of the work. The description adds nothing about the heresy taxonomy — the seven enum values (haste, severed_chain, whack_a_mole, etc.) are unexplained in both places — and does not clarify which parameters matter for which action.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names three concrete capabilities — recording freestyle mistakes (confess), viewing the Wall of Shame, and tracking heresy statistics — which map cleanly onto the action enum. The thematic framing ("Litany of Unbinding", "heresy") is decorative but doesn't obscure the underlying record/view/aggregate triad, and it is distinguishable from siblings like mcp_m7_litany or mcp_m7_error_database by its self-audit focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"AI confesses when it deviated from M7 methodology" implies the trigger for the confess action, but nothing tells the agent when to use wall_of_shame or stats versus alternatives such as mcp_m7_error_database or mcp_m7_validate. No exclusions or sibling routing are given, so usage is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.