sonar_list_alertsList your alert subscriptions — each rule defines a change type (rank drops, review spikes, etc.), its scope (a specific app or org-wide), threshold, and whether it's enabled. Requires an Indie plan (trial counts).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint: true. The description adds domain knowledge about what alert rules contain, but does not disclose additional behavioral traits such as pagination, rate limits, or data freshness. It does not contradict annotations.
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: first states the action and explains what alert rules are, second provides a prerequisite. No wasted words, front-loaded with the core purpose. Highly efficient.
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 no parameters, no output schema, and annotations present, the description is fairly complete. It explains the tool's output and a key constraint. It could possibly hint at the output structure, but overall it's adequate for an agent to select and invoke the tool correctly.
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?
There are no parameters, so schema coverage is 100%. The description adds value by explaining the meaning of the returned data (change type, scope, threshold, enabled). Baseline for 0 params is 4, which is appropriate here.
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 'List your alert subscriptions' with a specific verb and resource. It explains what each rule defines (change type, scope, threshold, enabled), distinguishing it from sibling tools like sonar_set_alert and sonar_delete_alert. No ambiguity.
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 ('Requires an Indie plan'), which is useful context, but it does not explicitly state when to use this tool versus alternatives or when not to use it. The purpose is clear but usage guidance is only implied.
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.