Compare 2-5 crypto casinos side by side on WagerX audit data: trust score, latest real-money live test, KYC policy, withdrawal speed, license. Use for "Duelbits vs BC.Game?", "Which is safer, Stake or Rollbit?", "bspin or Bitsler for fast payouts?". Example call: {"names": ["Duelbits", "Stake"]}.
| Name | Required | Description | Default |
|---|---|---|---|
| names | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the metrics compared (trust score, KYC, withdrawal speed, license) but does not specify behavior for invalid names, exact name matching requirements, output format, or rate limits. Some transparency is present, but gaps remain for a read/comparison tool.
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?
The description is two sentences plus an example call, with no filler. Every sentence adds value: first states scope and data fields, second gives use cases, third shows exact JSON format. Highly efficient and front-loaded.
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 tool with one parameter, no output schema, and no annotations, the description adequately explains the input but lacks details about the output (e.g., format, what 'side by side' means) and error handling for missing casino names. It is complete enough for basic use but leaves open questions for an AI agent.
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 description coverage for the 'names' parameter. The description adds meaning by specifying it is an array of 2-5 casino names, listing example queries, and providing a JSON example call. This adds clear semantic value beyond the bare schema.
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 verb 'compare' and the resource 'crypto casinos' on specific WagerX audit data (trust score, live test, KYC, etc.). It distinguishes the tool from siblings like 'check_casino' (single casino) and 'top_casinos' (ranking) by specifying side-by-side comparison of 2-5 casinos.
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 provides explicit example queries ('Duelbits vs BC.Game?', 'Which is safer, Stake or Rollbit?') that tell the agent exactly when to use this tool. While it does not list exclusions or alternatives, the usage context is clear and directly tied to comparative questions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.