Get one hotel's cosy score, evidence signals and description from Got Cosy, by its slug (as returned in find_cosy_hotels' results, or from a gotcosy.com/en/hotels/ URL). Below-floor hotels return {below_bar:true} with no score exposed.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The hotel's Got Cosy slug |
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?
Discloses special behavior for below-floor hotels. No annotations provided, so description carries burden. Could mention authentication or rate limits, but fine for a simple read operation.
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, no fluff. Front-loaded with purpose. Efficient and well-structured.
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?
No output schema, but description adequately covers return values (score, signals, description) and edge case. Complete for the tool's simplicity.
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?
Schema coverage is 100% with one parameter described. Description adds value by explaining how to obtain the slug from find_cosy_hotels results or URL, beyond the schema's brief description.
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?
Clearly states verb 'get', resource 'one hotel's cosy score, evidence signals and description', and input 'slug'. Distinguishes from sibling tool 'find_cosy_hotels' by specifying slug source.
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?
Explicitly mentions when to use (by slug from find_cosy_hotels or URL) and describes edge case (below-floor hotels return {below_bar:true}). Provides clear context for usage.
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 targets a distinct operation: searching for hotels vs. retrieving details for a specific hotel. No overlap in functionality.
Both tools follow a consistent verb_noun pattern: 'find_cosy_hotels' and 'get_hotel_feeling'. While 'find' and 'get' are different, they are both common action verbs and the pattern is uniform.
With only 2 tools, the set is minimal but appropriate for a niche service focused on accessing a cosiness index. It covers the core needs of searching and retrieving details without unnecessary bloat.
The tool set provides all necessary operations for the domain: searching with filters and retrieving detailed information by identifier. No obvious gaps as the service is read-only and focused on a single entity type.
