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evalstate

MCP Webcam Server

by evalstate
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TypeScript
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The tools have overlapping purposes—both capture visual data from the user's environment—with 'capture' targeting the webcam and 'screenshot' targeting the screen, but descriptions could lead to confusion as 'capture' mentions examining objects the human shows, which might overlap with screen content. The boundaries are somewhat unclear, especially for agents interpreting use cases.

    Naming Consistency4/5

    Tool names follow a consistent verb-based pattern ('capture' and 'screenshot'), both being single words describing the action. There are no deviations in style or casing, making them readable and predictable, though 'screenshot' is more specific than 'capture' in terms of naming convention.

    Tool Count2/5

    With only 2 tools, the server feels under-scoped for a webcam domain, as it lacks operations like video capture, settings adjustment, or multi-camera support. This minimal set may limit agent functionality, making it borderline too few for comprehensive visual input handling.

    Completeness2/5

    The tool surface is significantly incomplete for a webcam server; it covers basic image capture from webcam and screen but misses essential operations such as starting/stopping video, configuring camera settings, or handling multiple inputs. This creates gaps that could lead to agent failures in more complex visual tasks.

  • Average 4/5 across 2 of 2 tools scored. Lowest: 3.4/5.

    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 status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

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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 readOnlyHint=true and openWorldHint=true, so the agent knows this is a safe, non-destructive operation with open-world assumptions. The description adds minimal behavioral context beyond this, such as specifying it captures the 'current screen or window', but doesn't detail aspects like format, size, or potential limitations. No contradiction with annotations exists.

    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, clear sentence that efficiently conveys the core functionality without any wasted words. It is front-loaded with the essential information, making it easy for an agent to parse and understand quickly.

    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 simplicity (0 parameters, no output schema) and rich annotations (readOnlyHint, openWorldHint), the description is adequate but minimal. It covers the basic purpose but lacks details on output format or behavioral nuances that could aid the agent, such as whether it returns an image file or data. It meets minimum viability but has gaps in completeness.

    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?

    The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's action. A baseline of 4 is applied since it avoids redundancy and adds value by explaining the tool's purpose without unnecessary details.

    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 tool's purpose with a specific verb ('Gets') and resource ('screenshot of the current screen or window'), making it immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'capture', which might have overlapping functionality, preventing a perfect 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 the sibling tool 'capture', nor does it mention any prerequisites, context, or exclusions. It merely states what the tool does without offering usage instructions, leaving the agent to infer when it's appropriate.

    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?

    Annotations provide readOnlyHint=true and openWorldHint=true, indicating safe, non-destructive operation with potential for varied outcomes. The description adds valuable context by specifying it captures from 'the webcam' and returns 'the latest picture,' clarifying the source and immediacy of the data, which goes beyond what annotations alone convey.

    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 front-loaded with the core purpose in the first sentence, followed by usage guidelines in a clear, efficient manner. Every sentence adds value without redundancy, making it appropriately sized and well-structured for quick comprehension.

    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 (0 parameters, no output schema), the description is complete enough for effective use. It covers purpose, usage guidelines, and behavioral context. The absence of an output schema is mitigated by the description's clarity on what is returned ('the latest picture'), though more detail on output format could enhance completeness.

    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?

    The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, maintaining focus on tool functionality. A baseline of 4 is applied since there are no parameters to document.

    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 ('Gets the latest picture from the webcam') and resource ('webcam'), distinguishing it from the sibling tool 'screenshot' which likely captures screen content rather than camera input. The verb 'Gets' is precise and the resource is unambiguous.

    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 provides when-to-use guidance with concrete examples: 'if the human asks questions about their immediate environment,' 'if you want to see the human,' or 'to examine an object they may be referring to or showing you.' This gives clear context for selecting this tool over alternatives like 'screenshot'.

    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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  • Evaluate tool definition quality.

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