Taste

arbitrate_dispute

Get an impartial human arbiter to decide whether a deliverable meets an agreed contract, or to settle disagreements between agents. Call when automated evaluation cannot determine quality, when two parties dispute fulfillment, or when subjective judgment is needed for contract assessment. Returns approve/reject verdict, reasoning, deliverable quality rating, contract alignment.

Input Schema

TableJSON Schema

Name	Required	Description
`context`	No	Optional context. Use to clarify intent, constraints, audience, or anything that helps the expert evaluate.
`termsUri`	No	Where the att/v1 manifest is hosted (https/ipfs). Convenience only — the hash is the identity.
`termsHash`	No	Terms anchor this dispute is bound to: keccak256 of the att/v1 terms manifest (as echoed from the x402 payment), or an opaque 32-byte document hash such as an LCP atrHash ("sha256:0x…" accepted). Persisted on the ruling for the receipt + precedent lookups.
`termsJson`	No	The full att/v1 terms manifest object. If provided it must be a valid manifest whose keccak256 equals termsHash.
`deliverable`	Yes	The deliverable to assess. Text or a publicly accessible URL.
`partyAPosition`	Yes	One side of the dispute. The expert evaluates both impartially — order is arbitrary.
`partyBPosition`	Yes	The other side of the dispute.
`contractDescription`	Yes	The agreed contract or deliverable specification.

Output Schema

TableJSON Schema

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

TDQS

A4.2/5.0

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 that the arbiter is impartial, returns specific outputs (verdict, reasoning, quality rating, contract alignment), and is for dispute resolution. It does not cover potential limitations or costs, but overall it provides good transparency.

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 (4-5 sentences), front-loads the purpose, and every sentence adds value. There is no fluff or redundancy.

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 complexity (8 params, output schema exists, many siblings), the description covers the core purpose, usage conditions, and outputs. It does not explain the output schema details but that is acceptable since the schema exists. It is mostly 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 coverage is 100%, so baseline is 3. The description does not add additional meaning beyond the schema; it only restates the purpose of each parameter indirectly. No extra semantics or context are provided.

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 gets an impartial human arbiter to decide whether a deliverable meets a contract or settle disagreements. It specifies the situations for use and lists the return values (verdict, reasoning, quality rating, alignment). This distinguishes it from sibling tools like ask_a_human, which are more general.

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 'Call when...' and lists three specific scenarios (automated evaluation cannot determine quality, parties dispute, subjective judgment needed). It provides clear context but does not explicitly mention when not to use it or list alternative tools, though the sibling list implies alternatives.

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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