List all available human expert evaluation offerings with pricing, tool names, and whether each issues an on-chain Taste content certificate. Free.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| usage | Yes | ||
| status | Yes | ||
| service | Yes | ||
| offerings | Yes | ||
| nextOpenAt | Yes | ||
| onlineExperts | Yes | ||
| operatingHours | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description partially carries the burden. It discloses output fields but does not mention side effects, auth needs, or data freshness. It is adequate for a read-only list.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no unnecessary words. Information is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no parameters and an output schema, the description is complete. It covers the tool's purpose and output details sufficiently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With zero parameters and full schema coverage, baseline is 4. The description adds no parameter info because none exist.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists all available human expert evaluation offerings, specifying included details (pricing, tool names, certificate). It distinguishes from sibling tools like list_expert_profiles by the nature of offerings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. The description only states what it does, without mentioning prerequisites or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.