Search the Apple Ad Repository for apps or developers advertising on the EU App Store. Returns ids to use with get_competitor_ads. Start here with your competitor's app name.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | App or developer name to search for | |
| types | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds that the search is limited to the EU App Store and returns IDs for downstream use, which is helpful context but does not disclose potential limitations like result limits or case sensitivity.
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 concise sentences with no wasted words. It front-loads the tool's purpose and closes with a clear next step, making it easy for an agent to quickly parse.
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 search tool with no output schema, the description covers the core functionality, return value (IDs), and a clear starting use case. However, it omits details on the 'types' parameter and potential search behaviors like pagination or wildcards, which would improve usability.
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 only 50% (name has a description, types does not). The description mentions 'your competitor's app name' to clarify the 'name' parameter, but does not address the 'types' parameter beyond its enum values. Given the low coverage, the description provides partial but insufficient compensation.
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 tool searches the Apple Ad Repository for apps or developers on the EU App Store, and specifies it returns IDs for use with get_competitor_ads. It uses a specific verb ('Search') and resource ('Apple Ad Repository'), distinguishing it from sibling tools like get_competitor_ads and get_ad_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 advises to 'Start here with your competitor's app name,' providing a clear entry point for competitor analysis. However, it does not explicitly mention when to avoid this tool or list alternative search methods, though the context of the sibling tools implies its primary role.
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 clear, distinct purpose. The status checkers (assemble_status, clips_status) are differentiated by the job type they monitor. Memory tools (index, note, recall) are distinct operations. Competitor ad tools (search, get ads, get details) and video generation pipeline (avatar, clips, assembly) are well-separated.
All tools use consistent snake_case with a verb_noun or noun_verb pattern (e.g., assemble_video, get_competitor_ads, memory_index). No mixing of conventions, making the tool names predictable and easy to understand.
With 17 tools, the server covers account management, memory, competitor research, and video generation without being overwhelming. The count is well-scoped for its functionality, neither too sparse nor too heavy.
The toolset covers the core workflows: account setup (sign_up, upgrade, whoami), memory (CRUD), competitor analysis (search, get ads, details), and video ad creation (avatar, clips, assembly). Minor gaps like lacking a delete memory tool are acceptable given the domain focus.
