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Edplayerone

iwatch-mcp

by Edplayerone
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Python
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Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool serves a distinct purpose: one returns the most recent single reading, one retrieves historical data over a time window, and one streams live data. No overlap.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern using snake_case: get_heart_rate, get_heart_rate_history, stream_heart_rate.

    Tool Count5/5

    Three tools is appropriate for heart rate access: current reading, history, and streaming. Not too few nor too many.

    Completeness4/5

    CRUD-like coverage: read single, read history, stream. Minor gap: no way to query a specific time range outside 'past N hours' or duration.

  • Average 4.4/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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

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  • 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?

    No annotations provided; description lacks details on error conditions, permissions, or what happens if no data is available. The return format is described but not behavioral traits.

    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?

    Short, front-loaded with purpose, and efficiently lists return fields without extraneous text.

    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?

    With no parameters and an output schema (assumed), description explains return fields fully. For a simple getter, it is 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?

    No parameters; schema coverage is 100% vacuously. Baseline for 0 params is 4. Description adds no extra parameter info necessary.

    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 it gets the most recent heart rate reading from Apple Watch. Siblings get_heart_rate_history and stream_heart_rate indicate distinct use cases.

    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 usage for the latest reading, but does not explicitly state when to use this vs siblings or mention any exclusions.

    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?

    With no annotations provided, the description carries full burden and effectively discloses behavioral traits: buffering of last 5 minutes, network-dependent latency (10-60 s), and that it runs HealthKit observer. No contradictions; only minor omission of error conditions.

    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?

    Description is well-structured: first sentence states purpose, followed by details, a note on latency, and clear Args/Returns sections. Every sentence is necessary and front-loaded.

    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 one optional parameter, no enums, and an output schema (not shown but mentioned as JSON array), the description covers return format and key behavior. Could mention error handling or prerequisites (e.g., Apple Watch availability) but still above average.

    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?

    Despite 0% schema coverage, the description adds context beyond the schema: duration_seconds has a valid range of 1-300 and a default of 30. This compensates for the schema's lack of description.

    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 specific verb 'collect' and resource 'heart rate readings from Apple Watch', clearly distinguishing from sibling tools 'get_heart_rate' (likely single reading) and 'get_heart_rate_history' (historical data) by focusing on streaming over a time window.

    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?

    While the description implies it's for real-time monitoring with buffered readings and latency explanation, it lacks explicit guidance on when to use this tool versus siblings. Usage context is inferred but not stated.

    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 covers behavior: returns all samples in past N hours, sorted oldest-first, from Apple Watch and HealthKit sources. It also describes the return format (JSON with bpm/timestamp/source/device).

    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 concise (5 sentences) with a clear front-loaded purpose, followed by parameter details and return format. Every sentence contributes meaning without redundancy.

    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 simple one-parameter tool, the description is complete: it explains the function, input, output, data sources, and constraints. The existence of an output schema is acknowledged but not required for completeness here.

    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?

    The parameter 'hours' is explained with its meaning, default, and maximum value, adding significant value beyond the schema which only provides type and default. This compensates for the 0% schema description coverage.

    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 verb 'retrieve' and resource 'heart rate history' from macOS HealthKit. It specifies the time window (past N hours) and sorting, distinguishing it from siblings like get_heart_rate (single point) and stream_heart_rate (real-time).

    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 explains the purpose and parameter constraints (default 24, max 168 hours), implicitly guiding use for historical data. However, it lacks explicit when-to-use vs alternatives or exclusions.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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