Taste

review_code

Get a vetted human engineer to review your code, architecture, and design decisions — not just style, but correctness, security, and whether the structure will hold up. Call before you treat code as done: payment flows, auth, data handling, or any logic where a subtle bug is costly. Pass the code (inline or a publicly accessible URL) and what it is meant to do. Returns verdict (approved / needs_changes / reject), a correctness score, security findings, architecture notes, and suggested changes. Approved code receives a Taste content certificate on-chain.

Input Schema

TableJSON Schema

Name	Required	Description
`code`	Yes	The code to review. Paste it inline, or pass a publicly accessible URL (e.g. a gist or raw file).
`intent`	Yes	What the code is meant to do — the behaviour and constraints, plus any correctness or security concerns to focus on.
`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

A4.5/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses the return verdict, correctness score, security findings, architecture notes, and on-chain certification. It does not mention rate limits or auth, but the core behavior is well-described.

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 a single well-structured paragraph that front-loads the purpose, then usage, parameter guidance, and output. Every sentence adds essential information without redundancy.

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 (human in the loop, output schema present), the description covers input, process, output, and certification. The context signals show high schema coverage and presence of output schema, so the description is sufficiently complete.

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%, so baseline is 3. The description adds value by specifying that 'code' can be a URL, 'intent' can include focus areas, and 'context' is optional for extra info. This goes beyond the schema 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 explicitly states it gets a human engineer to review code, covering correctness, security, and architecture. It clearly distinguishes from sibling tools like 'review_content' and 'consult_domain_expert' by focusing on code review.

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 strong guidance on when to use: 'Call before you treat code as done: payment flows, auth, data handling, or any logic where a subtle bug is costly.' It does not explicitly state when not to use, but the positive context is clear.

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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