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huegli

Capture Win MCP

by huegli
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Python
Local

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: list_windows enumerates available windows with organizational details, while capture_window takes a specific window ID to capture an image. There is no overlap in functionality, and the descriptions clearly differentiate their roles, making misselection unlikely.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (list_windows, capture_window), using snake_case throughout. The naming is predictable and aligns well with their actions, providing clear and uniform identification.

    Tool Count4/5

    With only 2 tools, the server is minimal but appropriately scoped for its purpose of window listing and screenshot capture. It feels slightly thin, as additional related operations (e.g., capturing the entire screen or managing windows) could enhance coverage, but the core workflow is supported without bloat.

    Completeness4/5

    For the domain of window management and screenshot capture, the tools cover the essential workflow: listing windows to obtain IDs and capturing specific windows. A minor gap exists, such as the inability to capture the entire screen or multiple windows at once, but agents can effectively use the provided tools without dead ends.

  • Average 3.8/5 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
    • 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.

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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 carries full burden. It mentions the tool returns 'detailed information about windows, spaces, and which windows belong to which Space,' which gives some behavioral insight. However, it lacks critical details like whether this requires permissions, how data is sourced (real-time vs cached), performance characteristics, or error handling. For a tool with no annotations, this is insufficient.

    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 two sentences with zero waste: the first states the purpose and scope, and the second specifies the return content. It's front-loaded with the core functionality and appropriately sized for a simple tool.

    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 tool's low complexity (1 optional parameter, no output schema, no annotations), the description is minimally complete. It covers what the tool does and what it returns, but lacks behavioral details that would be helpful for an agent (e.g., permissions, data freshness). Without annotations or output schema, it's adequate but has clear gaps.

    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 description coverage is 100%, so the schema fully documents the single parameter 'format' with its enum values and default. The description adds no parameter-specific information beyond what the schema provides, which is acceptable given the high coverage. Baseline 3 is appropriate as the schema handles the heavy lifting.

    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 verb ('List') and resource ('windows organized by macOS Space'), specifies the scope ('all windows'), and distinguishes from the sibling tool 'capture_window' by focusing on listing rather than capturing. It's specific and unambiguous.

    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. It mentions the sibling tool 'capture_window' exists but gives no context on when to choose listing over capturing or other potential scenarios. Usage is implied but not explicitly stated.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format ('Returns the image as base64-encoded PNG') which is valuable, but doesn't mention potential limitations like window visibility requirements, performance impact, or error conditions. It provides basic behavioral context but could be more comprehensive.

    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 perfectly concise with two sentences that each serve distinct purposes: the first states the tool's purpose and return format, the second provides usage guidance. There's no wasted language and it's front-loaded with the core functionality.

    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 moderate complexity (screenshot capture with parameters), no annotations, and no output schema, the description does well by explaining the return format and workflow. However, it could provide more context about potential constraints or error cases. It's mostly complete but has minor gaps.

    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 description coverage is 100%, so the schema already fully documents both parameters. The description mentions window IDs come from 'list_windows' which adds some context, but doesn't provide additional semantic meaning beyond what's in the schema descriptions. This meets the baseline for high schema coverage.

    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 specific action ('Capture a screenshot') and target resource ('a specific window by its ID'), distinguishing it from the sibling tool 'list_windows' which provides window IDs. It uses precise verbs and resources without being vague or tautological.

    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?

    The description explicitly states when to use this tool ('Capture a screenshot of a specific window by its ID') and provides a clear alternative/pre-requisite ('Use list_windows first to get window IDs'). This gives complete guidance on tool selection and workflow.

    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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