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ysntony

AppsFlyer MCP Server

by ysntony
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one fetches aggregate data reports for analytics, while the other tests the API connection for operational status. There is no overlap or ambiguity in their functions, making it easy for an agent to select the correct tool based on the task.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (get_aggregate_data, test_appsflyer_connection), using snake_case and clear, descriptive verbs. This uniformity aids in predictability and readability across the tool set.

    Tool Count2/5

    With only two tools, the server feels thin for an AppsFlyer analytics domain, which typically involves more operations like querying specific metrics, managing campaigns, or handling user data. The limited scope may hinder agents from performing comprehensive tasks, suggesting an underdeveloped surface.

    Completeness2/5

    The tool set is severely incomplete for an AppsFlyer analytics server. It lacks essential operations such as retrieving detailed reports, filtering data, managing integrations, or performing CRUD actions on resources. This gap will likely cause agent failures in handling typical analytics workflows beyond basic data fetching and connection testing.

  • Average 3/5 across 2 of 2 tools scored.

    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
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • 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 carries the full burden of behavioral disclosure. It states 'fetches aggregate data reports' which implies a read-only operation, but doesn't mention authentication requirements, rate limits, data format, pagination, or error handling. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

    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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded with the core action, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's complexity (fetching data from an external API with multiple report types), lack of annotations, no output schema, and poor parameter documentation, the description is insufficient. It doesn't explain what 'aggregate data reports' contain, how they're structured, or provide any context about the AppsFlyer API integration.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters1/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The description provides no information about parameters, while the schema has 0% description coverage (the schema's internal descriptions don't count toward this metric). With 1 required parameter (a nested object with 4 sub-parameters) and no parameter details in the description, it fails to compensate for the schema's lack of documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('fetches') and resource ('aggregate data reports from the AppsFlyer Pull API'), providing a specific purpose. However, it doesn't differentiate from the only sibling tool 'test_appsflyer_connection', which appears to be a connection test rather than a data retrieval tool, so the distinction isn't explicitly made.

    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 or any context for its application. It mentions the source ('AppsFlyer Pull API') but doesn't specify scenarios, prerequisites, or exclusions, leaving usage entirely implicit.

    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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool tests connection and returns server status, but lacks details on error handling, timeouts, authentication requirements, or what 'server status' entails (e.g., HTTP codes, latency). For a connectivity tool with zero annotation coverage, this leaves significant gaps in understanding its operational behavior.

    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 a single, clear sentence: 'Test the connection to AppsFlyer API and return server status.' It is front-loaded with the core purpose, has zero redundant information, and efficiently communicates the tool's intent without unnecessary elaboration.

    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 tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks context on usage scenarios, error handling, or output details (e.g., format of 'server status'). For a connectivity test tool, more behavioral transparency would enhance completeness, though the absence of parameters reduces complexity.

    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?

    The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, so it meets the baseline for tools with no parameters. No additional value is required beyond stating the tool's function.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Test the connection to AppsFlyer API and return server status.' It specifies the verb ('Test'), resource ('AppsFlyer API'), and outcome ('return server status'), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'get_aggregate_data', which appears to be a data retrieval function rather than a connectivity test.

    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 doesn't mention prerequisites (e.g., authentication setup), timing (e.g., before data operations), or contrast with the sibling tool 'get_aggregate_data'. The implicit context is testing API connectivity, but explicit usage scenarios or exclusions are absent.

    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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