Fetch the full detail page for a specific AI tool by its linkName. Returns name, full description, category, screenshot, and additional information if available.
| Name | Required | Description | Default |
|---|---|---|---|
| linkName | Yes | The canonical linkName of the application (the slug used in /application/<linkName>). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses return contents (name, full description, category, screenshot, additional info) and uses 'Fetch' to imply a read-only operation. It lacks edge-case details like not-found behavior, but this is acceptable for a simple read 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?
A single sentence clearly states purpose, input, and output with no filler. It is front-loaded and every clause adds value.
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 one-parameter read tool with no output schema, the description is sufficient: it states the input, the action, and the returned fields. It could mention error handling, but the low complexity and strong schema coverage make the description complete enough for selection and invocation.
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 already fully documents 'linkName' with 100% coverage, including its canonical slug format. The description adds no new semantic detail beyond restating that lookup is by linkName.
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 uses a specific verb 'Fetch' and clearly identifies the resource: the full detail page for a specific AI tool by its linkName. This distinguishes it from sibling tools like search_tools and browse_category, which handle lists or categories.
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 clearly implies the use case—retrieve details for one known tool by linkName—and contrasts with broader browsing/searching tools. However, it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Each tool has a clearly distinct purpose: browsing a category, comparing two tools, finding alternatives, fetching tool details, getting top picks, listing categories, and searching. There is no ambiguity or overlap.
All tool names follow a consistent verb_noun pattern in snake_case (e.g., browse_category, compare_tools, get_tool). The naming is predictable and uniform across the set.
With 7 tools, the set is well-scoped for an AI tool directory. Each tool serves a specific and necessary function, and there is no unnecessary bloat or deficiency.
The tool surface covers all core use cases for a directory: browsing by category, searching, getting details, comparing, and finding alternatives. Minor gaps like missing 'top trending' or user-specific features are acceptable and do not hinder typical workflows.
