get_toolGet the full profile of one AI tool by its directory slug: description, pricing, key features, editorial verdict and rating, the date it was last human-verified, lifecycle status, and the official site URL.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The directory slug, e.g. "gamma-app-ai-powered-presenting-ideas" (from search_tools). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds value by detailing what fields are returned (editorial verdict, rating, last verified date, lifecycle status, site URL), which helps an agent understand the behavioral outcome beyond the annotations.
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 a single, well-structured sentence that front-loads the action and tool specificity. Every word adds value with no 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 (one required param, read-only, no output schema), the description fully covers what the tool does and what it returns. No gaps remain for an agent to select or invoke it correctly.
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 a clear parameter description for 'slug' including an example. The description only mentions 'by its directory slug' without adding new meaning. Thus the description adds little beyond the schema, justifying a baseline 3.
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 'Get the full profile of one AI tool by its directory slug' and enumerates specific fields returned (description, pricing, key features, etc.). It explicitly distinguishes from siblings like compare_tools, find_alternatives, and list_tools by focusing on a single tool's full details.
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 implies usage when you have a slug from search_tools, but does not explicitly state when not to use it or provide exclusion criteria. However, the purpose is clear enough that an agent can infer appropriate contexts.
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 distinct purpose: checking status, comparing two tools, finding alternatives, getting a full profile, listing by category, and searching. No overlap.
All tool names follow a consistent verb_noun pattern in snake_case (check_tool_status, compare_tools, find_alternatives, etc.), making it predictable.
6 tools are well-scoped for a directory server, covering discovery, lookup, comparison, and status checks without being too few or too many.
The tool set fully covers user needs: search, list, get details, compare, find alternatives, and check lifecycle status. No obvious gaps for the intended purpose.