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gamercc-apps

Mock Screenshots MCP Server

by gamercc-apps
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JavaScript
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.6

  • Disambiguation5/5

    Each tool has a distinct purpose: generating a fake chat screenshot, listing device frames, and listing chat platforms. No two tools overlap in functionality.

    Naming Consistency5/5

    All tools use a consistent verb_noun pattern in snake_case (generate_fake_chat, list_devices, list_platforms), making the naming predictable and easy to understand.

    Tool Count5/5

    With three tools, the server is appropriately scoped for generating fake chat screenshots, covering the main action and necessary discovery. It is neither too sparse nor overloaded.

    Completeness4/5

    The tool set covers the core generation workflow and listing of options. A minor gap is the lack of additional customization tools, but the current surface is functional for the stated purpose.

  • Average 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
    • 19 commits in the last 12 weeks
    • No stable releases found
    • 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.

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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, so description must carry full burden. It only states the basic action with no disclosure of side effects, authentication needs, or output behavior (e.g., format, pagination).

    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?

    Single, front-loaded sentence with no wasted words. Efficiently conveys the tool's purpose.

    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?

    With no parameters and no output schema, the description covers the basic purpose but omits details about return format or data structure, which would help an agent interpret results.

    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?

    Zero parameters (baseline 4), but description adds meaning beyond the empty schema by specifying the content (iPhone/Android device frames) and context (the generators support). This fully compensates for lack of schema detail.

    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?

    Description clearly states the verb 'List' and the resource 'device frames (iPhone and Android) the generators support'. It distinguishes from sibling tools: generate_fake_chat (generation) and list_platforms (platforms).

    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?

    No explicit guidance on when to use or when not. Context implies use when device frame information is needed, but no comparisons to siblings or prerequisites are mentioned.

    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 provided, so description carries full burden. It discloses that output is watermarked and clearly fictional, but does not detail other behavioral aspects like authentication, rate limits, or side effects. The parameter schema is rich, but description adds only the watermarking point.

    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 front-load the core purpose and add necessary usage warnings without redundancy. Every part contributes value.

    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 7 parameters and no output schema, the description explains that output is a deep link and watermarked. It covers the essential context for using the tool, though it could elaborate on the return structure for different formats.

    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 descriptive property descriptions. The description does not significantly add meaning beyond the schema, except for reinforcing the platform list. Baseline of 3 is appropriate.

    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?

    Description clearly states the tool composes a fake chat screenshot for various platforms and provides a pre-filled deep link. It mentions specific use cases (parody, education, design mockups, fiction) and distinguishes from siblings by focusing on generation rather than listing devices or platforms.

    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?

    Description explicitly states the output is watermarked and intended for legitimate purposes, not deception. While it gives usage context, it does not directly contrast with sibling tools (list_devices, list_platforms) that could be used to explore available options.

    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?

    No annotations provided, so description carries full burden. Discloses that it returns generator URLs, indicating a safe read operation. Lacks details on pagination or limits but sufficient for a simple list.

    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?

    Single concise sentence, no waste, front-loaded with purpose.

    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 no parameters, no output schema, and a simple tool, the description is complete. Tells what it lists and what it returns.

    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, baseline 4. Description adds no parameter info, but none needed.

    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 lists chat platforms and their generator URLs. Distinct from sibling tools generate_fake_chat and list_devices.

    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?

    No explicit guidance on when to use vs alternatives, but the purpose is straightforward and implies usage to view available platforms.

    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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