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Guitarmaniac24

GetABrain🧠 | Live Human-in-the-Loop MCP for AI Agents

rate_response

Rate a worker's response on a scale of 1-5 to provide feedback that influences their quality score and future eligibility. Optionally add free-text comments.

Instructions

Rate a single worker's response 1-5 to feed the worker quality/reputation system, optionally with free-text feedback. Use after reviewing a response from get_responses/wait_for_responses, to reward good answers and flag poor ones -- this affects the worker's quality score and future eligibility (e.g. queries with min_worker_quality set) and can trigger rewards/suspension server-side. Side effect: writes a rating record and returns the updated worker quality score; does not resubmit or modify the original response. Disambiguation: rates a response you already have -- does not fetch new responses (use get_responses/wait_for_responses first).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoreYesQuality rating for the response, 1 (worst) to 5 (best). Feeds the worker's ongoing quality score.
query_idYesThe id of the query the response belongs to (from submit_query).
response_idYesThe id of the specific response to rate (from get_responses/wait_for_responses output).
feedback_textNoOptional free-text comment explaining the rating, visible to the worker.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNo
successNo
worker_quality_scoreNo
Behavior5/5

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

The description adds significant context beyond annotations: it writes a rating record, returns updated quality score, does not resubmit or modify original, and mentions server-side rewards/suspension. No contradiction with annotations (readOnlyHint=false, destructiveHint=false).

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 the main action and is fairly concise. However, the first sentence is long and could be slightly streamlined without losing clarity.

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 moderate complexity (4 parameters, output schema exists), the description covers purpose, usage context, side effects, and disambiguation. The existence of an output schema means return values do not need to be explained.

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%, so the description adds minimal extra meaning. It mentions 'free-text feedback' which matches 'feedback_text', but beyond that, the schema already explains all parameters adequately.

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 uses specific verbs ('rate', 'feed') and clearly states the resource (worker response). It distinguishes from sibling tools 'get_responses' and 'wait_for_responses' by noting it does not fetch new responses.

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?

Explicitly states when to use ('after reviewing a response from get_responses/wait_for_responses'), explains the effect on worker quality and future eligibility, and disambiguates from fetching tools.

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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