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Reliability Profile

reliability_profile

Generates a read-only behavioral profile of a member's participation and judgment metrics, based on observable review actions without affecting decisions.

Instructions

Read-only derived behavior profile for a member (v2.2, metric 2.2-r3).

An OBSERVATIONAL statistic, not a verdict on anyone: participation (observable facts — heartbeat/gate state, awaiting verdicts, comment_to_verdict & vote_coverage rates; no endogenous ground truth) and judgment (episode-local five-way classification of every objection: revision_absorbed / active_overridden / frozen_unadjudicated / wontfix / continued, self-loops excluded from the positive; object_rate with the numerator pinned to first-verdict stance per (author, revision); stance flips split by whether they cross a revision; co_objection vs lone). verdict_free and verdict are the same judgment event (billing differs).

Raw components only — NO composite score, by protocol. Zero governance weight: never enters any decision path (behavior-invariance tested); pull-only; derived on read over a query_only connection (no writes possible at the SQLite layer, 附录 F2).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
authorYesthe member to profile (your own name or another's).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It explicitly states it is read-only, derived over a query_only connection with no writes possible, and that it provides raw components only with no composite score. It also notes it has zero governance weight and is behavior-invariance tested. This is exceptionally transparent about side effects and limitations.

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?

The description is front-loaded with a clear one-line purpose, then expands into detailed technical specifications. It is dense but every sentence adds value, covering the types of data, the classification scheme, and the operational constraints. It is longer than a trivial description but proportionate to the tool's complexity.

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

Completeness5/5

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

Given the complexity of the tool, the description is thorough. It explains what the profile contains, the semantics of the metrics, the distinction between verdict_free and verdict, and the no-write guarantee. The output schema exists, so return format is covered. Nothing essential for correct invocation is missing.

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?

The input schema already fully describes the single parameter 'author' with 100% coverage, including the note that it can be one's own name or another's. The description adds no additional semantic information about the parameter itself. Since schema coverage is complete, the baseline of 3 is appropriate.

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

Purpose5/5

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

The description states a specific purpose: 'Read-only derived behavior profile for a member.' This is a clear verb+resource combination that distinguishes it from sibling tools like get_protocol or set_verdict, which are about other aspects. The observational nature is emphasized, so an agent can immediately understand this is a non-mutating query.

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

Usage Guidelines4/5

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

The description conveys when to use it: for inspecting a member's behavior profile without affecting anything, as it is 'pull-only' and 'never enters any decision path.' It doesn't explicitly contrast with sibling tools, but the read-only, observational framing makes the intended context clear. The absence of explicit alternatives is a minor gap, but the context is strong enough to guide usage.

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