snapdiff-mcp

Server Quality Checklist

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Latest release: v0.4.2
Disambiguation3/5
There is overlap between snapdiff_compare_pages (baseline compare mode) and snapdiff_verify_ui_change, both comparing against a baseline. Also snapdiff_capture_screenshot and snapdiff_capture_baseline both capture screenshots, but baseline is project-specific. Descriptions help disambiguate but some tools have similar functionality.
Naming Consistency4/5
All tools share the 'snapdiff_' prefix and follow a verb_noun pattern (capture_baseline, capture_screenshot, check_build, compare_pages, verify_ui_change). 'html_to_image' deviates slightly but remains clear. Overall consistent.
Tool Count5/5
With 6 tools, the set covers core visual testing workflows (baseline capture, ad-hoc screenshots, page comparison, build checks, HTML rendering, and verification with intent) without being bloated. Appropriate for the domain.
Completeness4/5
Covers main visual testing operations. Missing explicit baseline management (list, delete) or approval tools, but the review_url in verify_ui_change suggests human approval is external. Minor gap but workflow is functional.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.5/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 12 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
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  ]
}
```
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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?
No annotations are provided, so the description must disclose behavioral traits. It states 'Take a screenshot' which implies read-only, but does not elaborate on side effects, permissions, rate limits, or response format. For a simple screenshot operation, this is minimally acceptable 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?
Two sentences, zero wasted words. First sentence states the action. Second sentence lists use cases. Information is front-loaded and efficient.
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?
The description covers the basic purpose and usage context. However, given the presence of sibling tools with overlapping purposes, it does not hint at when to choose this tool over alternatives. No information about return values or output, which might be inferred but is not explicit.
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 baseline is 3. The description adds no parameter-specific information beyond the schema; it merely states the overall action. No extra semantics are provided.
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?
Description clearly states tool takes a screenshot of a web page and lists use cases (inspect layout, verify rendering, capture baseline). It partially distinguishes from sibling 'snapdiff_capture_baseline' by emphasizing current state, but the phrase 'capture a baseline for later comparison' overlaps with that sibling's likely purpose, preventing a 5.
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?
Provides a clear when-to-use context: 'when you need to see what a webpage looks like right now.' However, it does not mention when not to use this tool (e.g., for baseline comparison or verification) nor direct users to alternatives among siblings, leaving the agent without explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description does not disclose any behavioral traits such as whether the tool is destructive, rate limits, authentication needs, or limitations on HTML/CSS support. This is a significant gap for a tool with no annotation coverage.
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 concise with two clear sentences, front-loading the purpose and use cases. No unnecessary information.
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?
The tool has no output schema, so the description should explain what is returned (e.g., image format, URL or binary). It does not. Also missing constraints on HTML content (self-contained, external resources, size limits). However, for a simple rendering tool, the core purpose is clear.
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%, so the description adds limited value over the schema. It does provide default values for width and height (1200 and 630) and context about OG image standards, which is helpful but not essential.
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 renders HTML/CSS to an image with specific use cases like social cards and OG images. It distinguishes from sibling screenshot tools by noting 'without needing a hosted URL'.
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 explicitly says when to use (generating images from code) and hints at alternatives (for URLs use screenshot). However, it does not name sibling tools or provide explicit exclusions.
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; description carries full burden. It mentions that baseline is a reference screenshot and that the tool returns when confirmed, but does not disclose if calling multiple times overwrites the baseline or potential side effects. Adequate but not thorough.
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?
Four concise sentences with front-loaded purpose. No redundant information; every sentence adds 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?
All 4 parameters are described in schema, and description adds context for two of them. No output schema, but description mentions return behavior. Adequate for a tool with moderate complexity.
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?
Schema coverage is 100%, and description adds extra meaning: explains that 'url' parameter for localhost does not require a tunnel, and 'page_name' is used to match during verification. Exceeds baseline of 3.
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's purpose: capture current page state as a visual baseline for SnapDiff. It distinguishes from sibling tools like snapdiff_verify_ui_change by explaining it is a prerequisite.
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?
Explicitly says 'Call this once per page before using snapdiff_verify_ui_change' and explains difference between localhost and public URLs, giving clear guidance on when to use this tool.
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?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explains the tool captures pages, diffs against baselines, returns a per-page summary, and includes a review URL if changes are detected. It also notes that localhost URLs are captured locally without a tunnel. This adequately informs the agent about the tool's behavior without contradictions.
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 concise with no wasted words. It consists of two short paragraphs: the first defines the action and use case, the second describes the output. Every sentence adds value, and the structure is easy to parse.
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 has 3 parameters and no output schema, the description does a good job covering essential aspects: the batch nature, the return format (per-page summary), and the inclusion of a review URL. It also addresses the niche case of localhost. However, it does not explain what 'baselines' are or how to interpret the summary, but this is likely common knowledge in the domain.
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?
Although schema description coverage is 100%, the description adds meaningful context beyond the schema. It advises to 'Pass every route in your page map' for the pages parameter and clarifies that localhost URLs are handled without a tunnel. This helps the agent understand the intended usage beyond the raw parameter descriptions.
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 'Capture every page in the list and diff them all against their baselines in one build.' It uses a specific verb ('capture' and 'diff') and identifies the resource (pages in a project). The phrase 'in one build' distinguishes it from sibling tools like snapdiff_compare_pages, which likely handles individual comparisons.
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 provides explicit guidance: 'Use this after editing a shared component or design token to check whether the change affected pages you did not directly modify.' This tells the agent when to use the tool and implies that for single-page changes, a different tool might be more appropriate. However, it does not explicitly list alternatives or situations to avoid.
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?
With no annotations provided, the description takes the full burden. It explains the process (captures screenshots, pixel-level comparison, returns diff percentage and highlighted image) and the two modes. It does not disclose potential side effects (e.g., storing screenshots) or return format details, but covers the main behavior.
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 efficiently structured with a clear opening sentence, bullet points for modes, and every sentence adds necessary information. It is front-loaded with the core purpose, making it easy to scan.
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 complexity (8 parameters, two modes, no output schema), the description covers the essential aspects: modes, parameter usage, default behavior (branch defaults to default branch), and output (diff percentage and image). It could be more explicit about the exact return format, but overall it is complete.
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?
Schema description coverage is 100%, so baseline is 3. The tool description adds value by explaining the two modes, which connects how parameters like `before`, `project`, and `page_name` are used together. This context goes beyond individual parameter descriptions.
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's purpose: 'Visually compare two web pages to detect differences.' It lists specific use cases (verify code changes, compare staging vs production) and explicitly identifies itself as 'the primary tool' for visual verification, distinguishing it from its siblings.
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 provides clear usage context: 'use it whenever you need to verify visual output' and explains two modes (ad-hoc and baseline) with conditions for each. However, it does not explicitly state when not to use this tool or suggest alternatives beyond implying it is the primary one.
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?
With no annotations provided, the description carries the full burden. It discloses key behaviors: requires a project and page baseline, explains the verdict values (pass, expected_change_detected, etc.), and instructs the agent to surface review_url while not sharing raw image URLs. It also describes how intent_regions affect verdict accuracy. While not exhaustive (e.g., missing error handling or rate limits), it provides substantial context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action and usage guideline, then provides return value info, then parameter details. It is somewhat lengthy but each sentence adds value. Minor redundancy could be trimmed, but overall it is well-organized and easy to scan.
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 complexity (10 parameters, no annotations, no output schema), the description covers the return values and gives guidance on when to use optional parameters. It assumes baseline existence, which is reasonable given sibling tools. Some missing details (e.g., how to set up a baseline) are outside the scope of this tool's description, so completeness is good.
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?
Schema coverage is 100%, so baseline is 3, but the description adds significant value. For 'intent', it explains why it's required ('without it, the verdict cannot distinguish intended changes from regressions'). For 'intent_regions', it details the trade-off between selector and bbox, including how selector is resolved server-side. For 'match_tolerance_percent', it explains the default and when to adjust. This goes well beyond the schema.
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 starts with 'Verify a UI change you just made matches what you intended.' This clearly states the action and resource. It also explains when to use it: 'Use this whenever you modify a route, page, or component and need to confirm the visual result.' The purpose is specific and distinct from sibling tools like snapdiff_capture_baseline or snapdiff_check_build.
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 explicitly states when to use the tool: 'Use this whenever you modify a route, page, or component and need to confirm the visual result.' It does not explicitly mention when not to use it or provide direct alternatives, but the context is clear enough for an agent to understand the primary usage scenario.
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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