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pray

Halt repetitive failed edits and hung commands, then gather ground-truth evidence to restore a clean, testable baseline for coding agents.

Instructions

Summon the Altar of Ground Truth when standard debugging fails or repetitive loops occur. CALL THIS TOOL IMMEDIATELY WHEN: (1) A test or command fails 2+ times with similar errors; (2) An edited code change produced zero difference in output; (3) A terminal command or child process hangs/times out >15s; (4) You are guessing module exports or APIs; (5) You feel the urge to apologize for failed attempts. If auto_harvest is true (default), scans git status, lockfiles, and occupied ports automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
budgetNoMaximum remaining recovery rounds
problemNoConcise description of the stuck goal (optional if auto_harvest is true)
attemptsNoApproaches already attempted that failed to produce new evidence
request_idNoUnique client-generated idempotency key for this request
session_idNoOmit to start a new session; provide to resume an existing session
constraintsNoNon-negotiable invariants (e.g. cannot edit schema, cannot add dependencies)
heresy_modeNoExplicitly activate Heresy Mode to challenge foundational premises and suggest cheap falsification probes
project_keyNoUnique project identifier (e.g. repo name or workspace key)default
auto_harvestNoAutomatically scan git status, lockfiles, and occupied ports for ground-truth evidence
capabilitiesNoHost tool capabilities available (e.g. bash, read_file, git)
observationsNoHard, verified facts observed so far (logs, test outputs, diffs)
schema_versionNoContract schema version (default 1)
second_opinionNoRequest an adversarial second opinion / devil's advocate critique to challenge the proposed experiment
expected_revisionNoRequired when resuming a session to prevent stale concurrent updates
candidate_hypothesesNoPlausible explanations of the root cause to test

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose one real behavioral trait — that auto_harvest (default true) scans git status, lockfiles, and occupied ports — which is meaningful since it implies filesystem probing. However it never says what the tool returns, whether it mutates state, or how sessions/budget are consumed, leaving substantial gaps for an unannotated tool.

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?

Front-loaded with the trigger list and kept tight; the opening metaphor sentence is largely decorative but the actionable content dominates. Nothing is padded, though the cult framing costs a little clarity.

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

Completeness3/5

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

For a 15-parameter, no-annotation, no-output-schema recovery tool, the description covers triggering but omits what the call returns (guidance? next experiment? hypotheses ranking?) and never explains session-resume semantics or budget consumption, which are central to correct invocation.

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 description coverage is 100% across 15 parameters, so the schema already documents each field thoroughly. The description only elaborates on auto_harvest (repeating the schema's own wording) and does not add syntax or format meaning beyond it, so the baseline 3 applies.

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

Purpose3/5

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

The verb/resource framing relies on a metaphor ('Summon the Altar of Ground Truth') that never states plainly what the tool actually does operationally — presumably it returns a structured recovery/next-experiment proposal. The trigger conditions imply it is a debugging-unblock aid, but an agent cannot fully tell its purpose from the description alone, and the siblings report_outcome and inspect_ledger are not referenced.

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

Usage Guidelines5/5

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

It gives an explicit 'CALL THIS TOOL IMMEDIATELY WHEN' list with five concrete, observable trigger conditions (repeat failures, zero-diff edits, hangs >15s, guessing APIs, urge to apologize). This is a model example of when-to-use guidance for an agent.

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

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