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request_think_tank_revision

Spend a free revision turn on a completed think tank order. Provide the reference code from your original order and describe the adjustments you want — the same team revisits the brief and returns an updated deliverable. Poll get_result with the reference code to retrieve it. Free: no payment required, the turn is included with your order.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
adjustmentsYesWhat you want the team to adjust, expand, or rethink.
referenceCodeYesThe TASTE-... reference code returned when you placed the think tank order.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tipYes
statusYes
messageYes
sessionIdYes
referenceCodeYes
turnsRemainingYes

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the revision is free ('no payment required'), that the same team revisits the brief, and that the result is retrieved via 'get_result'. It does not specify error handling for invalid reference codes or missing revision turns, but the core behavioral traits are well covered.

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

Conciseness5/5

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

The description is concise, using three sentences to convey purpose, inputs, and follow-up action. It front-loads the core action ('Spend a free revision turn') and avoids unnecessary details. Every sentence earns its place.

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

Completeness4/5

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

Given the tool's simplicity (2 parameters, output schema exists), the description is mostly complete. It explains what the tool does, what parameters to provide, and how to retrieve the result. It lacks details on error scenarios or prerequisites (e.g., ensuring a free turn is available), which would make it more robust.

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

Parameters4/5

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

Schema coverage is 100% with clear parameter descriptions. The description adds value by explaining the process: 'the same team revisits the brief and returns an updated deliverable' and emphasizing the free nature. This context enriches the schema's bare descriptions, justifying a score above the baseline of 3.

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 explicitly states the tool's purpose: 'Spend a free revision turn on a completed think tank order.' It specifies the action (revision), the resource (think tank order), and the condition (free turn). This clearly distinguishes it from siblings like 'order_think_tank_session_30' (ordering new) and 'request_illustration_revision' (illustration revision).

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 provides clear context for when to use the tool: after a completed think tank order with a free revision turn. It also directs the user to poll 'get_result' to retrieve the updated deliverable. However, it does not explicitly state when not to use it (e.g., if no free turn remains) or compare it to alternatives like ordering a new session.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, covering different aspects of human expert evaluation: dispute arbitration, domain consultation, content review, certificate verification, etc. Even similar tools like review_content and prepublish_review differ in their focus (facts vs. cultural sensitivity), and order_think_tank_session_30 and _60 only differ by duration, which is natural.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in lowercase with underscores (e.g., arbitrate_dispute, list_offerings, verify_certificate). There is no mixing of conventions or vague verbs, making the naming predictable and easy for an agent to infer functionality.

Tool Count5/5

With 17 tools, the server strikes a good balance—enough to cover a wide range of human expert evaluation tasks without being overwhelming. Each tool serves a specific, justifiable purpose within the domain.

Completeness4/5

The tool set covers core workflows like ordering evaluations, retrieving results, requesting revisions, and verifying certificates. However, there is no explicit tool for ordering an illustration (only revision), which is a minor gap. Overall, the surface is nearly complete for the stated purpose.

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