App Store Connect MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool targets a distinct aspect of App Store Connect data: app listing, analytics, metadata, versions, reviews, downloads, and sales. There is no overlap in functionality; descriptions clearly differentiate them.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern: 'get_' for specific data retrieval and 'list_' for enumeration. The pattern is predictable and uniform.
Tool Count5/5
Seven tools cover the essential read-only operations for an App Store Connect server. The count is well-scoped, covering key data categories without being overwhelming.
Completeness5/5
The tool set provides comprehensive coverage for retrieving app data: analytics, metadata, versions, reviews, downloads, and sales. There are no obvious gaps given the read-only scope.
Average 4.1/5 across 7 of 7 tools scored. Lowest: 3.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on March 6, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It implies read-only via 'List' but does not explicitly state that, nor does it mention authorization needs, error behavior, or rate limits. The limit parameter's behavior is not elaborated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with two sentences, front-loading the core purpose. It is efficient, though could be more structured with separate usage and output sections.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of an output schema and annotations, the description partially covers what the tool returns (state, date, platform) but omits details on response structure, pagination, and error handling. It is adequate for basic use but not exhaustive.
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 descriptions for both parameters. The description adds value by indicating the source of appId ('from list_apps'), but does not enhance the limit parameter beyond what the schema already 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 the tool lists app versions with specific fields (state, date, platform) and specifies the source for the required appId. It is distinct from sibling tools which focus on analytics, metadata, or reviews.
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 gives a use case ('correlate releases with metric changes') and notes the prerequisite (appId from list_apps), but does not provide guidance on when not to use this tool or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Does not disclose whether tool is read-only, idempotent, requires authentication, or has rate limits. Only implies a read operation through naming and description.
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 contain all essential information: purpose, fields, use case, and prerequisite. No extraneous text.
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?
With one parameter and no output schema, the description adequately covers the tool's function. Missing details about output format or locale behavior, but overall complete for its simplicity.
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?
Only one parameter (appId) with schema description stating 'App Store Connect app ID (from list_apps)'. The description mentions this prerequisite, adding slight context beyond the schema, but schema coverage is already 100%.
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?
Clearly states the verb 'Get' and the resource 'localized metadata for an app', enumerating specific fields (title, subtitle, etc.) and providing use case (ASO review). Distinguishes from sibling 'list_apps' by requiring its output as input.
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?
Provides explicit context for use ('ASO review') and prerequisite ('Requires appId from list_apps'). However, does not explicitly state when not to use or mention alternatives among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It discloses that the tool returns structured rows with specific fields and auto-resolves vendor numbers from environment variables. However, it does not mention important behavioral traits like authentication needs, rate limits, or whether it is read-only.
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 two sentences, front-loading the core purpose and output, then adding a key detail about vendor number. Every sentence earns its place with no wasted 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 complexity (5 parameters, 3 required, 3 enums) and absence of an output schema, the description compensates by listing common output fields. It could mention default date behavior, but it is largely complete.
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%, so the baseline is 3. The description adds value by explaining the vendor number auto-resolution from environment variables and listing output fields, which are not 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 uses a specific verb ('Download and parse') and resource ('App Store Connect sales/installs reports'), clearly distinguishing it from sibling tools like 'get_analytics_report' or 'get_downloads_summary'. It also lists output fields, reinforcing its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It mentions automatic vendor number resolution but does not state prerequisites, limitations, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does not disclose pagination behavior or the effect of the 'limit' parameter; the phrase 'List all apps' conflicts with a limit parameter that defaults to 50, implying incomplete listing. No read-only hint is given.
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?
Three concise sentences with no fluff. The purpose is stated first, followed by return fields and usage guidance. Every sentence earns its place.
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 no output schema, the description explains return fields and the important usage of the ID. However, it lacks details about pagination, limit behavior, and potential empty results. Otherwise, it is fairly complete for a simple list 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?
The input schema already describes the 'limit' parameter with default, min, max, and type (100% coverage). The description does not add additional semantic 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 'List all apps in the App Store Connect account' and specifies the returned fields (id, name, bundleId, SKU). This distinguishes it from sibling tools which focus on analytics, metadata, versions, reviews, etc.
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: 'Use the returned id as appId in all other tools.' This implies it is the entry point for obtaining app IDs, though it could explicitly state to use this tool first.
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?
Discloses behavior: returns written reviews, aggregated stats, fetches iTunes rating, supports filtering by rating/territory/date, default sorting, and limit. No annotations provided, so description carries burden effectively.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Multiple sentences, each adding value. Structure is front-loaded with main purpose. Could be slightly more concise but not wasteful.
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?
Fairly complete given 8-param tool with no output schema. Covers sources, filtering, sorting, prerequisite. Lacks pagination details but limit implies single request. Adequate.
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%, but description adds context: explains storeCountry's purpose (iTunes Lookup API), territory as 3-letter code, and default sorting. Enhances understanding beyond 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?
Description clearly states it retrieves customer reviews and ratings, including aggregated statistics and iTunes API ratings. Differentiates well from sibling tools like list_apps and analytics tools.
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?
States prerequisite appId from list_apps and describes various filtering options. Does not explicitly compare to siblings, but purpose is distinct. Could mention when not to use.
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?
No annotations provided, so description carries full burden. Discloses granularity logic (daily for <32 days, monthly otherwise) and exclusion criteria for download types. Does not describe error handling or rate limits, but coverage is good for a simple query tool.
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?
Four sentences, each serving a distinct purpose: purpose, granularity, count scope, and requirements. No redundant or unnecessary information; highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description does not explain the return format or fields of the summary. It covers purpose, parameters, and behaviors, but lacks detail on response structure, which is a gap.
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%. Description adds value by specifying default values for startDate (3 months ago) and endDate (today), and clarifying that appId comes from list_apps. This enhances understanding beyond schema descriptions.
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?
Clearly states the tool gets first-time download counts aggregated by period. Distinguishes from siblings like get_sales_report by specifying it only counts new installs, excluding updates, re-downloads, and IAPs.
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?
Provides clear guidance on what the tool counts (new installs only) and requirements (ASC_VENDOR_NUMBER env var, appId from list_apps). However, it does not explicitly list when not to use it or compare to all siblings.
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?
With no annotations, the description fully discloses behavioral traits: default aggregated metrics, effect of raw=true, behavior of granularity (monthly vs daily), and the misleading mix without it. It also notes the return type differences and prerequisites.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and front-loaded with the main purpose. However, it is a bit lengthy; a slightly more concise presentation while retaining all critical information could improve it.
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's complexity (7 parameters, no output schema, no annotations), the description covers all necessary aspects: how to use, pitfalls, required inputs, and data nuances. It is fully complete for an AI 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.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 100% schema coverage, the description adds substantial meaning beyond the input schema: explains the raw parameter's effect, clarifies the importance and semantics of granularity, and provides context for dates. This greatly aids the agent in correctly invoking the tool.
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 that this tool retrieves analytics report data for an app and lists specific categories (APP_STORE_ENGAGEMENT, COMMERCE, etc.), which differentiates it from sibling tools like get_downloads_summary and get_sales_report.
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?
Provides explicit guidance on always setting granularity, explains the two-call strategy for full data, mentions required appId from list_apps, and warns against omitting granularity. This far exceeds the minimum requirement for usage guidelines.
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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