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peterparker57

Screenshot MCP Server

by peterparker57
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Server Quality Checklist

58%
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  • Latest release: v1.2.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'take_screenshot' has a single, clear purpose that cannot be mistaken for any other tool in this set.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'take_screenshot' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.

    Tool Count2/5

    A single tool is too few for a server with the apparent scope of screenshot functionality, which could reasonably include operations like capturing specific regions, saving in different formats, or managing screenshots. This minimal set feels thin and may limit agent capabilities.

    Completeness2/5

    The tool surface is severely incomplete for a screenshot domain. While 'take_screenshot' covers basic capture, there are obvious gaps such as saving screenshots to files, capturing specific areas, or handling multiple screenshots, which could lead to agent failures in more complex tasks.

  • Average 2.9/5 across 1 of 1 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. While 'take a screenshot' implies a read operation, it doesn't address permissions needed, whether it requires user interaction, file system access implications, or what happens on failure. The description lacks important behavioral context for a tool that interacts with the display system.

    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, efficient sentence that clearly states the tool's purpose with zero wasted words. It's appropriately sized and front-loaded with the essential information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, or important behavioral aspects like whether it requires specific permissions or has system dependencies. The description should provide more context given the complexity of interacting with display systems.

    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?

    With 100% schema description coverage, the input schema already documents all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline expectation but doesn't provide extra value.

    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 ('take') and resource ('screenshot') with specific scope options ('all monitors, specific monitor, or a specific window'). It's unambiguous about what the tool does, though without sibling tools to differentiate from, it can't achieve the highest differentiation score.

    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, prerequisites, or constraints. It simply states what the tool does without context about appropriate use cases or limitations.

    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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