Newest crypto casinos on the WagerX radar, including ones still mid-audit (clearly marked, never scored before the withdrawal test completes). Use for "new crypto casinos 2026", "newest bitcoin casinos worth trying", "casinos currently under audit". Takes no arguments.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses a key behavioral trait: results include casinos mid-audit, clearly marked and unscored until withdrawal test completes. This goes beyond a simple 'get new casinos' and adds safety context 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with zero waste: first identifies the resource, second adds behavioral nuance, third gives concrete query examples. 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?
For a zero-parameter list tool without output schema, the description covers purpose, usage, and the critical audit-status edge case. Given sibling differentiation and no missing info, it is complete.
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?
Input schema has zero parameters; description confirms 'Takes no arguments.' This adds certainty and matches schema coverage perfectly (100%). No further parameter information needed.
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 the tool returns 'newest crypto casinos on the WagerX radar', including those mid-audit. This distinguishes it from siblings like list_casinos (general list), top_casinos (ranked), and latest_audits (audit results) by focusing on recency and pending audits.
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?
Explicit usage examples are provided ('Use for...'), which define when to invoke the tool. However, no direct comparison or exclusion of sibling tools is given; the context clues are sufficient but not explicit.
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: bonuses, individual audits, comparisons, recent audits, full list, historical age, new entries, regulatory context, and top-ranked casinos. There is minimal overlap, and descriptions specify exact use cases for each.
All names follow a consistent snake_case style with descriptive prefixes (verbs or adjectives) followed by a resource noun (e.g., check_casino, best_bonuses). While not a uniform verb_noun pattern, the naming is predictable and readable.
Nine tools is well-scoped for a domain covering casino audits, bonuses, comparisons, regulatory info, and lists. Each tool contributes a distinct function without excessive overlap or redundancy.
The tool surface covers the full lifecycle of a user's needs: discovering casinos, evaluating safety via audits, comparing options, accessing recent data, and understanding regulatory context. No obvious gaps for the stated purpose.