Compare two AI tools side-by-side. Returns structured attributes for both (pricing, deployment, modality, content policy) so the client can recommend which fits a user's needs.
| Name | Required | Description | Default |
|---|---|---|---|
| linkName_a | Yes | The canonical linkName of the first application (slug used in /application/<linkName>). | |
| linkName_b | Yes | The canonical linkName of the second application. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It states the tool returns structured attributes, implying a read-only operation, and enumerates the attribute categories. However, it does not explicitly disclose whether it modifies data, requires auth, or handles missing tools. This is adequate but not exhaustive.
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 consists of two clear, concise sentences with no redundant wording. It front-loads the core function and then explains the returned data and purpose, earning a perfect score.
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 only two parameters and no output schema, the description is fairly complete: it explains the result (structured attributes) and lists the attribute categories. It does not mention error behavior or return format details, but these are not critical for a comparison tool.
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 description coverage is 100% with both parameters (linkName_a, linkName_b) well-defined. The description adds no additional parameter context beyond the schema, so the baseline score of 3 applies.
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 ('Compare') and resource ('two AI tools side-by-side'), and lists the returned attributes (pricing, deployment, modality, content policy). This clearly distinguishes it from sibling tools like get_tool (single tool) or get_alternatives (suggesting alternatives, not a direct comparison).
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 clear context for when to use the tool: when a client needs to recommend which of two tools fits a user's needs. It does not explicitly name alternatives or state when not to use it, but the comparison use case is evident.
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: 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.
