Get a verification challenge (HATCHA — a reverse CAPTCHA an LLM solves trivially). Required before submit_feedback or submit_check_feedback. Single-use, expires in 5 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that the challenge is single-use, expires in 5 minutes, and is trivially solvable by an LLM. This covers the key behavioral traits for a challenge token.
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?
Three concise sentences: the first defines the purpose and nature, the second states prerequisite context, the third states lifetime and usage constraint. Every sentence adds value without redundancy.
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 the tool's simplicity (no parameters, no output schema), the description covers purpose, prerequisite relationship, and expiration. It is fully sufficient for an agent to understand when and how to use this tool.
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?
The input schema has zero parameters, so schema coverage is 100%. The description adds no parameter-specific information because none exist. Per guidelines, baseline 3 is appropriate.
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 gets a verification challenge, defines the acronym HATCHA, and explains it is a reverse CAPTCHA for LLMs. It distinguishes itself from siblings by explicitly linking it to prerequisites for submit_feedback and submit_check_feedback.
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?
The description explicitly states the tool is 'Required before submit_feedback or submit_check_feedback,' giving clear context for when to use it and no ambiguity about alternatives. This sets expectations for a prerequisite step.
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.
Tools are generally well-distinguished, but discover_products and search_capabilities could be confused since both search across domains. The difference in query type (task intent vs. capability) is somewhat subtle. Overall, most tools have clear, separate purposes.
All tool names use a consistent verb_noun pattern (e.g., discover_products, get_checks, scan_domain). The naming is predictable and matches the server's domain of evaluating agent-ready properties.
With 11 tools, the server covers scanning, scoring, feedback, and search without being overly broad. Each tool has a distinct role, and the count is appropriate for the focused purpose of assessing agent readiness.
The tool set covers the full lifecycle: discover/scan domains, get scores and checks, apply skills, verify with challenges, submit feedback and disputes. There are no obvious missing operations for the stated domain.