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Rate served decisions (1-10) to improve future recommendations and report missing conventions for human curation.

Instructions

Report how helpful the served decisions were, and what was missing.

    Call this after a task where you used Metatron's decisions. Reference the
    `query_id` from the get_decisions_for_context output, then — most useful of all
    — **rate each served decision 1-10 by its [index]** in `ratings`, where 10 means
    it was exactly right and 1 means it was misleading. Also state any convention
    Metatron should have known but didn't, in `what_was_missing`.

    Behavior: ratings are 1-based indices into the decisions the named query served
    (they map to real decision ids locally, so you never echo a UUID; unknown indices
    and out-of-range scores are dropped). The graded scores feed a time-decayed,
    shrunk helpfulness signal that reorders which decisions get served first next
    time — helpful ones rise, misleading ones sink. A `what_was_missing` report is
    stored as a gap for a human-gated refiner to later reshape into a CANDIDATE
    decision. Nothing you send here promotes, demotes, or rejects a decision, or changes
    its wording — crossing the canonical set is always a human's call.

    Returns a short text confirmation that the feedback was recorded (and notes
    when a gap was captured for curation).
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idNoThe `query_id` token from the get_decisions_for_context output you are responding to.
helpfulNoOptional shorthand: 1-based indices of decisions that helped. Usually derived from `ratings`, so prefer `ratings`.
unhelpfulNoOptional shorthand: 1-based indices of decisions that were noise or misleading.
ratingsNoThe main signal: map of 1-based decision index (as a string) to a helpfulness score 1-10, where 10 = exactly right and 1 = misleading (e.g. {"1": 9, "2": 3}).
what_was_missingNoA convention Metatron should have known for this task but didn't. Captured as a candidate for human curation.
missing_scopeNoOptional file path or area the missing convention applies to.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: ratings affect a time-decayed helpfulness signal, unknown indices and out-of-range scores are dropped, what_was_missing is stored as a gap for human curation, and the tool does not directly promote/demote decisions. It also describes the return value.

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 well-structured with clear paragraphs covering purpose, usage, behavior, and return. It front-loads the main action. Every sentence adds value, though the description is somewhat lengthy; it could be slightly more concise but remains effective.

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 tool's complexity (6 parameters, no required) and the presence of an output schema, the description covers all aspects: purpose, when to use, parameter behavior, what happens internally, and the return value. It provides sufficient context for an agent to use the tool correctly.

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

Parameters5/5

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

Schema description coverage is 100%, providing a baseline of 3. The description adds significant meaning: it explains that ratings are 1-based indices with scores 1-10, clarifies that helpful/unhelpful are optional shorthands, and describes how what_was_missing is used. This goes well beyond the schema's descriptions.

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 clearly states the tool's purpose: 'Report how helpful the served decisions were, and what was missing.' It specifies the action (submit feedback), the resource (decisions from get_decisions_for_context), and distinguishes from siblings by focusing on feedback rather than fetching decisions (get_decisions_for_context) or proposing new ones (submit_candidate_decision).

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 explicitly says when to call: 'Call this after a task where you used Metatron's decisions.' It provides context (reference the query_id) and explains what to provide. While it doesn't explicitly state when not to use, it implicitly excludes use cases not involving feedback on served decisions.

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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