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honeybluesky

mcp-my-apple-remembers

by honeybluesky
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Python
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Server Quality Checklist

67%
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 is for retrieving information from various macOS sources (recall), and the other is for saving information specifically to Apple Notes (save). There is no overlap or ambiguity between these functions.

    Naming Consistency5/5

    Both tools follow a consistent 'my_apple_verb_memory' pattern, using the same prefix and suffix structure. The verbs 'recall' and 'save' are descriptive and aligned in style.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent scope of interacting with macOS data (notes, calendar, messages, files, etc.). A more comprehensive set would include tools for updating or deleting information, or handling other macOS apps beyond just Apple Notes.

    Completeness2/5

    The server is severely incomplete for macOS data management. It only covers recall (read) and save (create to notes), missing essential operations like update, delete, or interactions with other macOS apps (e.g., calendar events, messages). This will likely cause agent failures in broader workflows.

  • Average 3.1/5 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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?

    With no annotations provided, the description carries full burden but offers limited behavioral details. It mentions remote execution and timeout (implied via parameter) but doesn't disclose critical traits like authentication requirements, error handling, security implications, or what 'recall' entails operationally. The description adds some context but leaves significant gaps for a tool with mutation potential.

    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 reasonably concise with two sentences that cover purpose and usage. It's front-loaded with the core function, though the second sentence could be more structured. No wasted words, but minor room for improvement in clarity.

    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 (remote execution, potential data access), no annotations, and no output schema, the description is incomplete. It lacks details on return values, error conditions, security constraints, and operational limits, making it inadequate for safe and effective use by an AI agent.

    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 the schema already documents both parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (e.g., it doesn't explain AppleScript syntax or provide examples). Baseline 3 is appropriate as the schema does the heavy lifting.

    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: 'Run Apple Script on a remote MacOs machine' and specifies what it can recall (notes, calendar, messages, files, etc.). It distinguishes from the sibling 'my_apple_save_memory' by focusing on recall rather than save operations, though the distinction could be more explicit.

    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 context ('This call should be used to recall...') but lacks explicit guidance on when to use this tool versus alternatives or prerequisites. It mentions the sibling tool name indirectly but doesn't provide clear when/when-not rules or comparisons.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions executing on a remote machine and saving to Apple Notes, but doesn't cover important behavioral aspects like authentication requirements, error handling, what happens if the remote machine is unavailable, whether this is a synchronous or asynchronous operation, or potential side effects beyond note creation.

    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 reasonably concise with three sentences that each serve a purpose: stating the core function, specifying the intended use case, and providing implementation guidance. It's front-loaded with the main purpose. The third sentence could be more tightly integrated with the second for better flow.

    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?

    For a tool that executes remote AppleScript with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, error conditions, authentication requirements, or important behavioral constraints. The guidance about timestamps and note creation is helpful but insufficient for a remote execution tool with potential security and reliability implications.

    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 the schema already fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions AppleScript and saving to notes, which aligns with the code_snippet parameter purpose, but provides no additional semantic context about parameter usage or constraints.

    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: 'Run Apple Script on a remote MacOs machine' and specifies it's for saving information to Apple Notes. It distinguishes from the sibling tool 'my_apple_recall_memory' by focusing on saving rather than recalling. However, it doesn't explicitly contrast the two tools in the description text itself.

    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 provides some usage context: 'This call should be used to save relevant information to the apple notes' and gives implementation guidance about timestamps. However, it doesn't explicitly state when to use this tool versus alternatives or when not to use it. The sibling tool name suggests a recall function, but no explicit comparison is made.

    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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