App Store and Google Play ASO intelligence for AI agents. Search keyword opportunities and access your own tracked listings, rankings, review insights, and growth plans.
Theo's ranked growth plan for a tracked app -- what to fix first and why -- from the most recent plan generated on file.
| Name | Required | Description | Default |
|---|---|---|---|
| app_key | Yes | An app key from list_tracked_apps, e.g. 'ios:123456789'. | |
| country | No | Two-letter storefront code, e.g. 'us', 'de'. Defaults to 'us'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of disclosing behavior. It implies a read-only operation by referencing 'most recent plan generated on file', suggesting no side effects. However, it does not explicitly state that no changes are made, nor does it address rate limits or permissions. The transparency is adequate but not comprehensive.
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 a single, concise sentence that captures the essential purpose and data source without unnecessary words. It efficiently communicates the tool's value proposition and distinguishing features, making it easy to scan and understand.
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?
The description provides enough context for a straightforward retrieval tool: it names the output (ranked plan), the target (tracked app), and the source (most recent on file). While it doesn't explain what a 'tracked app' is or what to do if no plan exists, these are reasonable to infer from sibling tools and the schema. The absence of an output schema reduces the need for return-value details.
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 descriptions for both parameters are clear (app_key sourced from list_tracked_apps, country with format and default), giving high coverage. The tool description itself adds no extra parameter meaning, so the baseline of 3 applies. The schema already provides sufficient semantic clarity.
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 retrieves a ranked growth plan for a tracked app, including prioritization and reasoning. It distinguishes from siblings by focusing on the growth plan rather than listings, reviews, keywords, or app lists. The phrase 'most recent plan generated on file' adds specificity about the data source.
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 does not provide any guidance on when to use this tool versus its siblings. It does not mention prerequisites, such as requiring a prior plan, or scenarios where an alternative tool would be more appropriate. This lack of usage direction leaves the agent to infer context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The stored listing (name, subtitle, description, rating, price) for a tracked app.
| Name | Required | Description | Default |
|---|---|---|---|
| app_key | Yes | An app key from list_tracked_apps, e.g. 'ios:123456789'. | |
| country | No | Two-letter storefront code, e.g. 'us', 'de'. Defaults to 'us'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It describes a read operation ('stored listing') but does not explicitly state that it is read-only or has no side effects. No error behavior or data source is disclosed.
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 a single, concise sentence that immediately conveys the tool's purpose and the content of the returned listing. No filler or redundant wording.
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?
The list of returned fields gives useful context for the expected output, and the parameters are well-explained in the schema. Minor gaps include no mention of error cases or whether the listing is return-only, but the tool's simplicity keeps it mostly complete.
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 good coverage for both parameters: app_key has an example and country has a default and description. The description adds no extra semantic detail beyond the schema, so it meets the baseline.
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 retrieves a stored listing for a tracked app and lists the specific fields (name, subtitle, description, rating, price). The verb 'get' and resource 'listing' are explicit, and the tool is distinguished from siblings like get_growth_plan or list_tracked_apps.
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 provides no guidance on when to use this tool versus alternatives, such as when to prefer get_listing over list_tracked_apps or search_keyword. No context or preconditions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Per-storefront rating-floor verdict and recurring complaint themes for a tracked app, from the most recent review read on file.
| Name | Required | Description | Default |
|---|---|---|---|
| app_key | Yes | An app key from list_tracked_apps, e.g. 'ios:123456789'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations describing side effects, read-only status, or permissions. The description mentions reading from a file but does not explicitly disclose whether the operation is safe, idempotent, or has any impact on data.
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 a single, clear sentence with no redundant words. It efficiently conveys the tool's purpose and data source.
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?
The description is largely complete, stating what is returned and the data source. It could be slightly enhanced by defining what 'verdict' entails or how 'rating-floor' is calculated, but it is not necessary for basic 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 single parameter app_key is fully described with an example and reference to list_tracked_apps. The schema coverage is 100%, and the description adds meaningful context beyond the raw type.
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 provides a per-storefront rating-floor verdict and recurring complaint themes for a tracked app, based on the most recent review read on file. It is specific and distinct from sibling tools like get_listing or get_growth_plan.
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?
No explicit guidance is provided on when to use this tool versus alternatives. The description implies it is for review-related insights but does not state conditions or scenarios that would make it the preferred choice over sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The keyword watchlist for one tracked app: current rank, whether apps your size are reachable there, and (Standard+) difficulty and effectiveness.
| Name | Required | Description | Default |
|---|---|---|---|
| app_key | Yes | An app key from list_tracked_apps, e.g. 'ios:123456789'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of explaining side effects, permissions, or mutating behavior. It only describes the output content and does not disclose whether the tool is read-only, whether it can error, or any rate limits or auth requirements.
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 a single compact sentence that names the resource and enumerates the main output components without redundancy. It is efficient and easy to 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?
With no output schema present, the description helpfully enumerates the returned data categories (rank, reachability, difficulty/effectiveness). It is concise but lacks any mention of error cases or additional output metadata, so it is slightly incomplete for full context.
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 only parameter, app_key, is fully described with its source (list_tracked_apps) and a concrete example format. This gives enough context for an agent to populate it correctly.
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 identifies the resource as a keyword watchlist for one tracked app and lists the key data returned (current rank, reachability, difficulty/effectiveness). It distinguishes this from sibling tools that handle growth plans, listings, review verdicts, or keyword search, though it is phrased as a noun phrase rather than an explicit imperative.
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 does not state when to use this tool versus alternatives such as search_keyword or get_listing. It implies a read operation but provides no explicit selection guidance or conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List the apps tracked on this account, optionally filtered by platform.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No | Optional. Omit to list both platforms. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the verb 'List' strongly implies a read-only operation. The description mentions no side effects or mutations, which is appropriate for a listing tool.
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 a single, concise sentence that includes all necessary information without any extraneous words. It is well-structured and immediately understandable.
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 list operation with one optional parameter, the description is complete. It specifies the resource scope and the filter, and there is no output schema to describe, so nothing is missing.
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 description directly explains the only parameter ('platform') and its optional nature, matching the schema. Since the schema provides the enum values, the description adds just enough context without redundancy.
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 action ('List'), the resource ('apps tracked on this account'), and the optional filter ('by platform'). It is unambiguous and distinct from sibling tools which focus on other resources.
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 mentions the optional platform filter, telling the agent when to use it. It does not explicitly contrast with siblings, but since all siblings are getters for different resources, the intended usage is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 |
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_growth_planget_listingget_review_verdictget_tracked_keywordslist_tracked_appssearch_keywordClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
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Feature your server to boost visibility and reach more users
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Claim ownership of the server listing
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Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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.