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michaelahern

Airthings Consumer MCP Server

by michaelahern
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.1.0

  • Disambiguation5/5

    Only one tool exists, so there is no risk of ambiguity or confusion between tools.

    Naming Consistency5/5

    With a single tool, naming consistency is inherently maintained, even though the name 'airthings' is a noun rather than following a verb_noun pattern.

    Tool Count2/5

    The server has only one tool, which is too few for managing Airthings devices and sensor data; a typical scope would require multiple tools for different operations.

    Completeness2/5

    A single tool covering all device information and sensor readings lacks specificity and modularity; agents cannot perform targeted operations like retrieving data for a specific device or sensor type.

  • Average 3.2/5 across 1 of 1 tools scored.

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

    • 1 of 1 community issues answered or closed in the last 6 months
    • 5 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 disclose behavioral traits. It only states 'Get information,' implying a read operation, but does not mention authentication, rate limits, data freshness, or whether any side effects occur. This is insufficient for an agent to understand the tool's true 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 sentence, direct, and contains no redundant information. Every word contributes to conveying the tool's purpose, making it highly concise and well-structured.

    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 simplicity of the tool (no parameters, no output schema), the description covers the basic purpose. However, it lacks details about the output structure, such as whether it returns a list or single device, or what sensor readings are included. This makes it minimally adequate but not fully 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?

    The tool has zero parameters, and the schema coverage is 100% (by default). The description adds value by specifying what kind of data is returned (air quality device info and sensor readings), which goes beyond the empty schema. A baseline of 4 is appropriate for no parameters.

    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 verb 'Get information' and the resource 'Airthings air quality monitoring devices and their current sensor readings.' It is specific enough to convey the tool's purpose, though 'information' is slightly vague. No sibling tools exist to cause confusion.

    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 prerequisites. There are no siblings, but even without alternatives, the description lacks context such as intended use cases or frequency of calls.

    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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