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zelentsov-dev

ClipSnap

by zelentsov-dev
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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 completely distinct purposes: one deletes temporary images, the other retrieves recent images. There is no ambiguity or overlap.

    Naming Consistency4/5

    Both tools use snake_case with verb_noun pattern, though 'paste_recent' uses an adjective. The naming is mostly consistent and predictable.

    Tool Count3/5

    With only 2 tools, the server feels under-scoped for an image management utility. It is borderline but not an extreme mismatch.

    Completeness2/5

    The tool surface lacks obvious features like capturing, editing, or listing images, leaving significant gaps for typical image-related tasks.

  • Average 4.2/5 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
    • 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 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

  • Behavior3/5

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

    Annotations already indicate destructiveHint=true and idempotentHint=true. The description adds context about automatic cleanup but does not disclose further behavioral traits such as confirmation requirements or effects on other users. Given annotations, the description provides adequate but not rich behavioral context.

    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 sentences with no wasted words. The first sentence states the primary purpose, and the second provides usage guidance. Highly efficient 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 the tool's low complexity (2 optional params, no output schema) and annotations covering destructive and idempotent traits, the description covers purpose, usage frequency, and a parameter hint. It does not describe return behavior, but this is a minor omission for a simple cleanup tool.

    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%. The description adds meaning for the 'all' parameter ('use all=true to purge all sessions'), but does not enhance understanding of 'older_than_minutes' beyond the schema. With full coverage, baseline is 3.

    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 'Deletes temporary ClipSnap images from the session temp directory' using a specific verb and resource, and distinguishes itself from automatic cleanup, which effectively differentiates it from the sibling tool 'paste_recent'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    Explicitly states 'Automatic cleanup already runs on TTL/size limits, so this is rarely needed', providing clear guidance on when to use this tool versus relying on automatic cleanup. Also advises using 'all=true' for purging all sessions.

    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?

    Beyond the readOnlyHint annotation, the description explains the tool combines three sources, sorts by time, and notes clipboard watcher avoids Spotlight delay. This adds useful behavioral context.

    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 four sentences with no redundancy. Information is front-loaded: purpose in first sentence, usage guidance in third, parameter nuance in fourth. Every sentence is meaningful.

    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 read-only tool with no output schema, the description covers purpose, usage, and parameter behavior. It does not describe return format or error cases, but the readOnlyHint and simplicity make this acceptable.

    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?

    All three parameters are fully described in the input schema (100% coverage), including defaults and folder behavior. The description adds minimal additional meaning beyond what the schema already provides.

    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 tool shows recent images from multiple sources, with a specific verb and resource. It distinguishes from sibling tool 'cleanup_images' by focusing on retrieval vs cleanup.

    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?

    Provides explicit when-to-use examples ('show my last screenshots', 'look at my clipboard') and a usage alternative (pass folder). Lacks explicit when-not-to-use compared to sibling, but the examples are clear context.

    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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