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samsebeingener

VisBug MCP Bridge

by samsebeingener
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Server Quality Checklist

58%
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  • Latest release: v0.6.15

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_changes retrieves captured changes, apply_changes marks them as applied, and clear_changes empties the buffer. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with underscores: get_changes, apply_changes, clear_changes. The verbs are imperative and the noun is uniform, providing a predictable and clear naming scheme.

    Tool Count5/5

    With only 3 tools, the set is perfectly scoped for managing a single buffer of visual changes. Each tool serves a necessary and distinct function, and no tool feels extraneous or missing for this focused purpose.

    Completeness4/5

    The tool set covers the core operations for managing the changes buffer: reading, marking as applied, and clearing. While there is no tool to revert individual changes or directly apply them to files, those functions are intentionally handled externally, making the set complete for its defined scope.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 42 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

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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?

    With no annotations provided, the description must fully disclose behavioral traits. It states that the tool does not modify project files and operates on a buffer file. However, it lacks details on idempotency, error conditions, return values, or prerequisites. The description provides basic safety information but is not 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 extremely concise: one sentence for the core purpose and one sentence for an important clarification. Every word adds value, and the structure is front-loaded with the primary action. No redundancy or filler.

    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 simplicity (single optional parameter, no output schema, no annotations), the description adequately explains its role in the broader workflow. It clarifies the buffer location and the separation of marking from actual file modification. However, it could be improved by specifying the return value or confirmation behavior.

    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?

    The input schema covers the single parameter 'ids' with a description in the schema itself ("Indices of edits to mark. Empty = all."). The tool's description adds no additional meaning beyond what the schema already provides. With 100% schema description coverage, a baseline score 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?

    The description explicitly states the tool's function: marking changes as applied in the buffer. It also clarifies what it does not do (modify project files), which distinguishes it from related operations. The sibling tools get_changes and clear_changes have different purposes, so this description effectively differentiates the tool.

    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?

    The description explains the tool's role in the workflow by noting that project files are written by auto-apply or the manual /visbug-apply command. This provides context on when to use this tool (to mark changes) versus when other mechanisms handle actual file writing. However, it does not explicitly contrast with the sibling tools or state when not to use it.

    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?

    As no annotations are provided, the description carries full burden. It discloses the destructive nature (clears buffer) but omits details like whether changes are permanently lost or if the operation is reversible. Basic transparency but lacks depth.

    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 conveys the tool's purpose without extraneous words. It is front-loaded and concise.

    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 simplicity of the tool (no parameters, no output schema, no annotations), the description is adequately complete for basic usage. However, it could mention the impact on get_changes or apply_changes for fuller context.

    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?

    With zero parameters, the schema fully covers the parameter space. The description adds no parameter information, but none is needed. The baseline of 4 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?

    The description clearly states the verb 'clears' and the resource 'buffer of captured edits', making the tool's purpose unambiguous. It distinguishes from siblings (get_changes, apply_changes) by focusing on clearing rather than retrieving or applying.

    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?

    The description implies usage when the agent needs to discard captured changes, but it does not provide explicit guidance on when to use this tool versus alternatives like get_changes or apply_changes. No context on prerequisites or typical scenarios.

    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 are provided, so the description carries full burden. It discloses that data comes from VisBug on localhost and lists all returned fields. While it doesn't mention side effects (none expected for a read), it adds valuable constraints not in structured data.

    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 purpose and list returned fields. Every sentence adds value with no redundancy or waste.

    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 no output schema, the description adequately explains return format. Parameter is well-documented in schema. Sibling tools are mentioned but not contrasted; however, the overall context is sufficient for a simple retrieval 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?

    The schema already covers the parameter with description (100% coverage). The description does not add extra meaning beyond what the schema provides, so baseline 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?

    The description clearly states the tool returns visual changes captured by VisBug on localhost, and lists specific fields (CSS selector, property, old/new value, HTML tag, page URL). This verb+resource+scope is specific and distinguishes it from siblings (apply_changes, clear_changes).

    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?

    The description implies usage for retrieving visual changes from localhost. It doesn't explicitly state when not to use or mention alternatives, but the context is clear enough for basic selection.

    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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