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shaack

mcp-for-spotlight

by shaack
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation4/5

    The two tools have clearly distinct purposes: one searches the Spotlight index, the other reads metadata for a specific file. No meaningful overlap exists between search and metadata retrieval.

    Naming Consistency5/5

    Both tools follow the consistent 'spotlight_<operation>' convention: spotlight_search and spotlight_metadata. The naming pattern is clear and predictable for a server scoped to Spotlight functionality.

    Tool Count3/5

    With only 2 tools, the surface feels thin for a domain that could reasonably include other operations (e.g., file info, indexed-item lifecycle ops). However, the two tools cover the core search+metadata workflow reasonably for a Spotlight-focused server.

    Completeness3/5

    The pair covers the two primary Spotlight operations (search and metadata retrieval), which is the obvious core workflow. However, there are minor gaps—e.g., no tool for getting indexed status, no way to count results, or handling common variations like filtering by path—though agents can usually compose around these with the search tool.

  • Average 3.9/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • 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.

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

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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description reveals that it uses mdls under the hood and that omitting attributes returns everything, which is useful behavioral context beyond the schema. It notes the read-only nature ('liest') implicitly. However, no annotations are present, and it doesn't disclose error behavior for nonexistent files, non-indexed locations, or what happens when attributes are unavailable.

    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 compact sentences with zero waste. Front-loaded with the tool's purpose and efficiency. The example attribute names embedded in the description are helpful and concise.

    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?

    For a simple read tool with 2 well-described parameters and no output schema, the description adequately covers what's needed: what it reads, how attribute filtering works, and the default behavior. The behavior is fully specified given the schema coverage, though it could mention that results depend on Spotlight indexing availability.

    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 both parameters well-documented in the schema itself (path as absolute file path, attributes as array of attribute names with examples). The description adds a small clarification that omitting attributes returns all metadata, slightly augmenting the schema meaning but not substantially beyond it.

    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 (reads/Liest), the resource (Spotlight-indexed file metadata via mdls), and the behavior (all attributes by default, named subset otherwise). It's specific and distinguishable from spotlight_search, the sibling that performs searches rather than reading metadata for a given path.

    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 explains the attribute filtering behavior (all vs. specific named attributes) which informs usage, but it doesn't explicitly state when to prefer this over spotlight_search or what kind of use cases it serves. The tool itself is self-evident for reading metadata, but no exclusions or alternative-navigation guidance is provided.

    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 behavioral disclosure burden. It discloses output (file paths), the single-search-type constraint, and the Full Disk Access limitation for protected locations. However, it does not specify whether limit applies across all three modes, the default formatting, or that mdfind output could be huge without a limit.

    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 compact (two sentences) and front-loaded with the purpose. It packs purposeful detail into the caveat sentence. Minor inefficiency: the tool name is in German but the search-mode keywords are English, which is understandable but slightly awkward.

    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?

    For a search tool with 5 parameters at 100% schema coverage, the description is reasonably complete. It explains the three search modes, the output format, and the protected-locations caveat. It lacks an explicit mention of behavior when no search-type param is given (error vs empty), and doesn't contrast with spotlight_metadata, but these are minor gaps given good schema coverage.

    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 description coverage is 100%, so parameters are already documented in the schema. The description adds value by clarifying the mutual exclusivity of text/name/query and the requirement to choose exactly one, which goes beyond the individual parameter docs. It doesn't add much per-parameter detail but the schema coverage makes that acceptable.

    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+resource ('Durchsucht den macOS-Spotlight-Index (mdfind) nach Dateien') and clearly distinguishes three search modes (text, name, query). It also differentiates from the sibling tool spotlight_metadata by focusing on files/full-text, adding scoping clarity.

    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 gives clear context on when to use this tool (searching files via mdfind) and explicitly states that exactly one search type must be provided. It mentions the protected-locations caveat. However, it does not explicitly contrast against spotlight_metadata (the sibling) or state 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.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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