sonar_screenshot_devicesList the device sizes supported for app-store screenshot sets, with their canvas dimensions (the pixel coordinate space all layouts use) and which store each belongs to. Pick a device here before sonar_create_screenshot_set.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, so no contradiction. The description confirms it is a read-only listing operation by saying 'List the device sizes...'. It adds value by detailing what data is included (canvas dimensions, store), but does not describe output format or pagination, which is acceptable for a zero-parameter tool with no output schema.
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 sentences with no wasted words. The first sentence defines the tool's output, and the second sentence provides usage guidance. Every sentence serves a clear purpose.
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 has no parameters, no output schema, and annotations already signal read-only, the description covers the key behavioral aspects. It explains what devices are listed and why the agent should call it. A minor gap is not mentioning that the output can be filtered or ordered, but since there are no parameters, this is acceptable.
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 has zero parameters with 100% coverage, so there is nothing to compensate for. The description explains what the tool returns without needing to clarify parameters, earning a baseline above 3 by providing context about the returned data (canvas dimensions, store assignment).
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 lists supported device sizes for app-store screenshot sets, including canvas dimensions and store association. It distinguishes itself from siblings like sonar_list_screenshot_sets by focusing on device sizes rather than sets, and from sonar_create_screenshot_set by explicitly stating to pick a device here before creating a set.
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 explicitly guides the agent to use this tool before invoking sonar_create_screenshot_set, providing clear sequencing. It also implies this is a prerequisite step for screenshot set creation, which differentiates it from other listing tools in the sibling list.
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 action or data aspect within the ASO domain. Keyword-related tools are clearly separated by purpose (tracked keywords vs. research vs. suggestions vs. metrics), and competitive tools differentiate between reading landscape and generating new analysis. No two tools have overlapping functionality that would confuse an agent.
Naming is a mix of verb-first (e.g., sonar_add_screenshot, sonar_create_product) and noun-first patterns (e.g., sonar_app_keywords, sonar_competitor_landscape). While all use snake_case and the 'sonar_' prefix is consistent, the lack of a uniform verb_noun structure makes it harder to predict tool names. The pattern is readable but inconsistent.
With 47 tools, the server is over-scoped for a typical MCP server. Although the ASO domain is broad, many tools are granular (e.g., 10 screenshot tools, 10 keyword tools). This quantity exceeds the 25+ threshold for 'too many' as defined in the calibration, making it heavy for an agent to navigate comprehensively.
The tool surface covers the full ASO lifecycle: app discovery, keyword research, tracking and ranking, competitor analysis, revenue estimation, screenshot creation and export, alerts, and product management. There are no obvious gaps—every necessary operation for monitoring and optimizing app store presence is present, including both read and write actions.