Taste

prepublish_review

Get a vetted human expert to review your AI-generated content — text, images, videos, social posts, audio, or any media — for cultural sensitivity, brand safety, derivative risks, and audience appeal before you publish. Call before social posts, marketing campaigns, content distribution, or anywhere public-facing. Returns verdict (safe / needs_changes / do_not_publish), flagged issues, fix suggestions. Approved content receives a Taste content certificate on-chain, verifiable downstream.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`content`	Yes	The content to review. Text directly, or a publicly accessible URL for non-text media.
`context`	No	Optional context. Use to clarify intent, constraints, audience, or anything that helps the expert evaluate.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`tip`	Yes
`status`	Yes
`message`	Yes
`offering`	Yes
`priceUsdc`	Yes
`sessionId`	Yes

TDQS

A3.6/5.0

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 discloses that the review is done by a human expert and returns verdict, issues, and fixes. However, it does not mention potential latency, cost, content storage, or other behavioral traits that might be relevant for an AI agent.

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 three sentences with clear front-loading. It efficiently covers what, when, and what is returned without extraneous information. Slightly longer than ideal but remains well-structured and informative.

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 output schema exists (assumed to detail return values), the description sufficiently covers purpose, usage timing, and returned items. It lacks mention of potential async nature or human availability, but for a review tool it is reasonably complete.

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%, and the description adds no additional meaning beyond what the schema already provides for both the 'content' and 'context' parameters. With full coverage, baseline is 3, and no extra value is added.

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

Purpose4/5

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

The description clearly states the tool reviews AI-generated content by a human expert for cultural sensitivity, brand safety, and audience appeal. It mentions returning a verdict and on-chain certificate, which distinguishes it from generic review tools like review_content, though no explicit sibling differentiation is made.

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 states when to use the tool: 'Call before social posts, marketing campaigns, content distribution, or anywhere public-facing.' It does not mention when not to use or provide alternatives, but the guidance is clear and context-specific.

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.

Resources

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