Live one-off research for a search term: difficulty, traffic and effectiveness against whichever apps currently hold the top results. No history is saved -- use this to compare candidate terms before deciding what to track.
| Name | Required | Description | Default |
|---|---|---|---|
| term | Yes | The search term to evaluate. | |
| country | No | Two-letter storefront code, e.g. 'us', 'de'. Defaults to 'us'. | |
| platform | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that it is a live, one-off operation and that no history is saved. This is transparent about side effects (or lack thereof) even without annotations, assuring the agent it is non-persistent and read-only in nature.
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, no redundant wording. The purpose is front-loaded, and the usage guidance is integral. Every word adds value.
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 simple research tool with no output schema, the description sufficiently covers what it does, what it returns, and when to use it. No additional context is needed for correct invocation.
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 schema provides descriptions for term and country, but platform only has an enum without explanation. Tool description adds no additional parameter context. With 2 of 3 parameters documented, coverage is moderate but not high, and the missing platform description is a minor gap.
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 states a specific verb ('research') and resource ('search term'), and clearly lists the outputs (difficulty, traffic, effectiveness against top apps). It is distinct from the sibling get_* and list_* tools, making its purpose unambiguous.
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?
Explicitly says 'No history is saved -- use this to compare candidate terms before deciding what to track.' This gives clear when-to-use guidance and implicitly contrasts with the tracked-keyword tools, leaving no ambiguity about its one-off, comparative 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 targets a clearly distinct concern: growth plans, listings, review verdicts, tracked keywords, app lists, and live keyword search. There is no functional overlap between tools like get_tracked_keywords and search_keyword because one reports watchlist history and the other performs ad-hoc research.
Tool names follow a consistent verb_noun pattern: get_* for retrieving stored entities, list_* for enumerating apps, and search_keyword for live research. The naming is predictable and immediately conveys each tool's purpose.
Six tools is a well-scoped set for an ASO-focused server, covering the main read-only surfaces without redundancy or unnecessary bulk. Each tool earns its place and the count feels appropriate for the domain.
The tool surface covers the core ASO workflows: viewing tracked apps, listing metadata, review verdicts, keyword watchlists, and performing live keyword research. Given the apparent read-only analytics scope, there are no obvious missing operations.