App Store MCP Server

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool targets a distinct aspect of App Store data: batch profiles, individual profiles, search, charts, raw reviews, review digests, and screenshots. No overlap in purpose.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case, with 'app_store' as the common noun. Verbs like get, search, compare, digest are clear and predictable.
Tool Count5/5
Seven tools cover the essential App Store operations without being too few or excessive. The scope is well-scoped for a focused server.
Completeness4/5
Core CRUD-like operations are present (get, search, compare), along with reviews and screenshots. Minor gap: no developer info or historical ranking data, but the main use cases are well covered.
Average 4.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 31 commits in the last 12 weeks
- Last stable release on July 10, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, so the safety profile is covered. Description adds that results are 'slim' and lists included fields (id, name, developer, rating, price), which is useful beyond annotations. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. Front-loaded with purpose and action. Every sentence serves a purpose: first defines what it does and what it returns, second directs to alternative for more detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, all documented in schema, and output schema exists (as per context signals), the description provides enough context: purpose, return shape, and when to use alternative. A minor gap is that it doesn't mention the country or limit filtering, but those are in schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage, so baseline is 3. Description mentions 'by keyword' which aligns with the query parameter but does not add meaning for limit or country beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it searches Apple App Store by keyword and returns slim results. It distinguishes from sibling get_app_store_app by noting the latter provides full profiles. Specific verb 'Search' + resource 'Apple App Store apps'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to use get_app_store_app for full profile when needed, guiding when to use this slim version vs the alternative. Could note other siblings like get_app_store_charts for different use cases, but current guidance is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and open-world hints. The description adds that it returns up to 'limit' screenshots as image content blocks and mentions each is a full image in context, advising to keep the limit small. This complements the annotations 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—two sentences that front-load the main purpose and include key details about return type and usage advice. No superfluous words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema, the description clarifies that the tool returns image content blocks, but does not specify the format (e.g., base64, URL). It is mostly complete but could be slightly more explicit about the return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents parameters well. The description adds value by explaining the return behavior related to the 'limit' parameter and the purpose of screenshots, enhancing meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches an app's App Store screenshots as actual images, and specifies the use case of analyzing visual positioning and design. It is distinct from sibling tools which handle reviews, charts, and text data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for visual analysis but does not explicitly contrast with sibling tools or provide guidance on when to avoid using this tool. The decision to use it is left to the agent without further context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral context beyond annotations: best-effort sourcing, undocumented Apple feed, ~500 review cap per storefront, page-sourced fallback, and per-country data. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loads the main action, and conveys essential limitations without unnecessary detail. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description does not need to explain return values. It covers behavior, limitations, and per-country scope, making it complete for a moderately complex tool with 4 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All parameters are documented in the schema with clear descriptions. The tool description adds context about the feed cap and fallback, which indirectly relates to limit and country, but does not significantly enhance meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches recent public customer reviews for an app, specifies it is per-country and best-effort, and distinguishes it from sibling tools like compare_app_store_apps and digest_app_store_reviews by focusing on raw review retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (fetch reviews) and notes limitations (best-effort, capped feed, fallback), but does not explicitly compare with siblings or provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable behavioral context: page-sourced fields are best-effort, include_page_data triggers an extra request, and include_raw returns a large payload to be used sparingly. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the main action, and every sentence adds value. No redundant or unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of a thorough input schema, output schema, and annotations covering read-only and idempotent behavior, the description covers the main purpose and optional behaviors well. It could mention when to prefer sibling tools, but overall it provides sufficient context for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and descriptions are already good, but the description adds extra nuance: app_id_or_url can be numeric or URL, country defaults to URL's country or 'us', include_raw is 'large - only when normalized fields are not enough', and include_page_data is 'best-effort, one extra request'. This enhances understanding beyond schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states 'Fetch the full public App Store profile for one app by numeric ID or apps.apple.com URL', using a specific verb and resource. It clearly differentiates from sibling tools like search_app_store, get_app_store_reviews, etc., by focusing on a single app's profile.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use the tool (for one app by ID or URL) and explains optional parameters (include_page_data, include_raw) with their trade-offs. However, it does not explicitly contrast with sibling tools or state when not to use this tool, e.g., when to use search instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds valuable disclosure of error handling: returns per-app errors and partial failures. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with purpose, no filler. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given low complexity, existence of output schema, and rich annotations, description covers necessary behavioral aspects (error handling) and use case. It is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with parameter descriptions describing formats and defaults. Description does not add additional semantic value beyond schema for parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb 'Fetch', resource 'app profiles', and scope 'multiple apps in batch' for side-by-side comparison. Distinguishes from siblings like get_app_store_app (single) and search_app_store (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies use case for competitor comparison. Mentioning that apps that fail do not fail whole call gives usage context. Lacks explicit when-not-to-use or alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds critical context: the tool relies on an undocumented feed and is best-effort. This discloses potential flakiness not captured by annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states the action, second adds a crucial reliability caveat. No extraneous words, and the key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown but noted), the description covers what the tool does, its parameters, and a behavioral caveat. It is sufficient for an agent to understand and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds little meaning beyond the input schema, which already has 100% coverage with clear descriptions for all 4 parameters. The tool's overall purpose is stated, but parameter details are not enhanced.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and clear noun ('ranked top-chart apps') and identifies the scope ('for a storefront'), clearly distinguishing it from sibling tools that handle individual apps, reviews, or search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the data is 'best-effort' and from an 'undocumented Apple RSS feed', which advises agents to expect possible unreliability. However, it does not explicitly say when to use this instead of alternatives like search_app_store.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds crucial behavioral context: it uses MCP sampling to compress reviews, the digest is always English regardless of storefront language, and it requires sampling support. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only three sentences, front-loaded with the core action. Every sentence serves a purpose: function, differentiation, and requirement. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool complexity (5 parameters, output schema exists), the description fully covers what the tool does, its output format, language behavior, and usage constraints. It is complete for an agent to decide to invoke this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description echoes the `limit` parameter and mentions the digest language, but does not add substantial new meaning beyond the schema. The `focus` parameter, for example, is only described in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches reviews and compresses them into a structured digest (themes, complaints, praise, sentiment) via MCP sampling. It explicitly distinguishes from the sibling tool get_app_store_reviews by advising to use that for raw reviews. The verb 'digest' and resource 'App Store reviews' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use (to avoid context overload), when not to use (when raw reviews are needed, use get_app_store_reviews), and requirements (client must support MCP sampling or have server-side fallback). This provides explicit guidance on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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