sonar_get_appGet full details for one tracked app in the caller's Sonar workspace: store metadata plus up to 90 daily snapshots of rating, review count, version, and installs. Requires an Indie plan (trial counts).
| Name | Required | Description | Default |
|---|---|---|---|
| app_id | Yes | Sonar app UUID — the `id` returned by sonar_list_apps or sonar_create_product. NOT a store id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds value by specifying the data scope (up to 90 daily snapshots of specific metrics) and the plan requirement. This is beyond what annotations provide, giving the agent useful behavioral context without contradiction.
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?
Two sentences, each earning its place. The first sentence states purpose and data scope; the second adds the plan requirement. No fluff, perfectly concise.
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 parameter, no output schema), the description fully covers what the tool does, what data it returns, and a key prerequisite. The schema and annotations handle the rest, leaving no gaps.
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% and the schema description for app_id is already detailed (UUID from specific sources, not a store id). The description adds no additional parameter-level information beyond the schema, so a baseline score of 3 is appropriate.
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 it retrieves full details for one tracked app, including store metadata and up to 90 daily snapshots. This specific verb+resource pairing distinguishes it from sibling tools like sonar_list_apps (list) and sonar_track_app (create).
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 mentions a prerequisite (Indie plan), which helps the agent know when the tool is usable. However, it does not explicitly state when to use this vs alternatives or when not to use it. The context is clear but lacks exclusionary guidance.
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.