MCP Frontend Analyzer

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, but there is some overlap between lighthouse_analyze and compare_lighthouse (both Lighthouse), and between get_seo_recommendations and nextjs_code_review (both give recommendations). Descriptions help differentiate, but slight ambiguity remains.
Naming Consistency4/5
All tools use snake_case and follow a verb_noun pattern. Verbs vary (check, compare, get, lighthouse_analyze, nextjs_code_review) but the structure is consistent and readable.
Tool Count5/5
8 tools is well-scoped for a frontend analyzer. Each tool covers a distinct area (accessibility, bundle, metadata, etc.) without being overwhelming.
Completeness4/5
The set covers major frontend analysis areas: accessibility, performance, code quality, SEO, and Lighthouse. Minor gaps like security or form analysis exist, but core workflows are covered.
Average 3.8/5 across 8 of 8 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
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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?
The description mentions that analysis may take a few seconds, which is a useful behavioral hint. However, it does not disclose other traits such as being read-only, error handling for invalid URLs, or whether authentication is required. With no annotations, more details would be beneficial.
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?
Three concise sentences with no redundancy. The verb is front-loaded, and every sentence provides essential information without waste.
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 function and duration but lacks details on the return format or structure of scores. Since there is no output schema, the description should ideally specify what the return object looks like. Adequate for a simple tool but could be more complete.
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 describes each parameter. The description does not add additional meaning beyond restating the categories. Baseline 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 clearly states it runs Google Lighthouse analysis on a URL and returns scores for performance, SEO, accessibility, and best-practices. This distinguishes it from sibling tools like check_accessibility and get_seo_recommendations which focus on specific aspects.
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?
No guidance on when to use this tool versus siblings. For example, it doesn't mention that for detailed accessibility checks or SEO recommendations, other tools like check_accessibility or get_seo_recommendations might be more appropriate.
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?
The description indicates it analyzes code and returns results, but lacks details on behavioral traits such as being read-only, required permissions, or potential side effects. With no annotations provided, the description should carry the burden of transparency but falls short.
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 sentences, front-loading the purpose. No unnecessary words, and it is well-structured for quick understanding.
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 is adequate for a simple analysis tool, but lacks details about the output format and does not fully leverage the context of having many sibling tools. It could benefit from specifying that results are returned in a structured format.
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 parameter descriptions are adequate. The tool description adds context about the analysis dimensions but does not enhance understanding of parameter usage 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 clearly states the tool analyzes React/Next.js code for SEO, performance, and accessibility, and returns issues and suggestions. It distinguishes itself from sibling tools like check_accessibility and get_seo_recommendations by performing a combined review.
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 the tool is for a general code review covering multiple aspects, but it does not explicitly state when to use this tool versus more specific sibling tools. No guidance on prerequisites or context.
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 are provided, so the description must fully disclose behavioral traits. It only describes the scope of analysis (static code checks) but lacks details on permissions, side effects (e.g., does it modify code?), error handling, or output format. The description is minimal.
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, front-loaded with the verb phrase, and lists key features efficiently. No filler content.
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?
For a tool with 2 parameters and no output schema, the description adequately covers the purpose and checks performed. It mentions WCAG standards, which is helpful context. However, it could briefly note that it is a static analysis tool (not a runtime check) to set expectations.
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 baseline is 3. The description reiterates the schema's parameter roles ('Analiz edilecek kod', 'bağlamsal analiz için') without adding deeper semantics, such as code size limits or expected format.
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 analyzes React/Next.js code for accessibility (a11y) and lists specific checks (semantic HTML, heading hierarchy, etc.) per WCAG standards. This distinguishes it from sibling tools like check_bundle or lighthouse_analyze.
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 for accessibility analysis but does not explicitly state when to use this tool versus alternatives (e.g., compare_lighthouse for performance). No when-not-to or exclusion criteria are provided.
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?
Though the description implies a read-only analysis by saying 'analiz eder', it does not explicitly state behavioral traits like idempotency or side effects, and no annotations are provided to compensate.
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 efficiently convey purpose and metrics list; no redundancy and front-loaded with key 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 description explains the tool outputs a 0-100 score but does not detail return format, error handling, or prerequisites. With no output schema, more detail would be beneficial for 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?
Schema description coverage is 100% with clear parameter definitions. The description adds value by enumerating the quality metrics checked, which enriches the understanding of what the code and filename parameters are used for.
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 analyzes React/Next.js component code quality and lists specific metrics, distinguishing it from siblings like check_accessibility or check_bundle.
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?
No explicit guidance on when or when not to use this tool versus alternatives; it merely describes functionality without contextual recommendations.
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 are provided, so the description must disclose behavioral traits. It indicates an analysis/read-only operation, but does not explicitly state that it does not modify anything or mention any side effects. The lack of detail on permissions or return behavior reduces transparency.
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, no filler. The most important information is front-loaded. Efficient and to the point.
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 is an analysis tool and the description lists specific checks, it provides sufficient context for an agent to understand the tool's capability. However, lack of output schema specification means the return format is not described, slightly reducing completeness.
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 context about the analysis (bundle size checks) but does not elaborate further on the parameters beyond what the schema already provides.
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: analyzing the impact of a React/Next.js file on JavaScript bundle size. It lists specific aspects checked (heavy imports, barrel exports, etc.), distinguishing it from sibling tools like check_accessibility or check_component_quality.
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?
No explicit guidance on when to use this tool versus alternatives. While the purpose is clear, the description does not provide when/when-not-to-use scenarios or mention sibling tools for comparison.
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 bears full burden. It clarifies the tool is read-only ('kontrol eder' meaning checks) and specifies what elements are inspected. This is sufficient for a validation tool, though it does not mention side effects or error 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?
Two sentences, front-loaded with the main purpose, and each sentence adds value—first the overall function, then the specific items checked. No wasted words.
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 tool's function and inspected items but omits any mention of output (e.g., return value, report format) even though no output schema exists. For a validation tool, this is a minor gap but leaves room for interpretation.
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% with descriptions for all three parameters. The description adds no additional meaning beyond what the schema provides (e.g., it does not explain code, filename, or pageType further). 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 checks SEO metadata completeness of Next.js page/layout files and enumerates specific metadata fields (title, OpenGraph, Twitter card, etc.). This verb+resource specification distinguishes it from sibling tools like check_accessibility or get_seo_recommendations.
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 use when verifying SEO metadata, but does not explicitly state when to use this tool versus alternative sibling tools (e.g., get_seo_recommendations). No exclusions or usage context beyond the core purpose.
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 are provided, so the description carries the burden. It discloses analysis duration (~20-60 saniye) and that scores are shown side-by-side, but does not mention auth requirements, rate limits, or potential side effects. The time estimate adds value but more depth is needed.
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 with no redundant information. The main purpose is front-loaded, and each sentence adds value.
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 comparison purpose, categories, devices, and time estimate. However, without an output schema, it does not detail the report format or whether scores are numerical or descriptive, leaving some ambiguity for the agent.
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% with descriptive parameter names and descriptions. The tool description adds context about typical use (production vs staging) but does not enhance meaning beyond the schema. 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 compares two URLs using Google Lighthouse and produces a comparison report. It specifies the verb (analyze and compare), resource (URLs via Lighthouse), and output (comparison report). It distinguishes from siblings like lighthouse_analyze which likely handles single URLs.
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 comparing two versions (mevcut vs yeni) but does not explicitly state when not to use it or mention alternatives. While the context is clear, there is no direct guidance on sibling differentiation.
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, description should compensate; it states it does not run Lighthouse, which is useful, but does not disclose other behaviors like whether it is a simple query or requires prior setup, or what the response format is.
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 sentences: the first states the purpose and scope, the second clarifies a key differentiator. No 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?
For a simple tool with one optional parameter, the description covers purpose, scope (React/Next.js), and key differentiator (no Lighthouse). It lacks explicit mention of output structure, but context signals show no output schema, so it's acceptable.
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 schema fully documents the parameter, but the description adds context (React/Next.js specific, no Lighthouse) that clarifies the tool's scope beyond the enum values.
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 SEO, performance, and accessibility recommendations for React and Next.js projects, explicitly noting it does not perform Lighthouse analysis, which distinguishes it from sibling tools like lighthouse_analyze and compare_lighthouse.
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 sets a clear context (general best practices without Lighthouse) and implies use cases (SEO, performance, accessibility), but does not explicitly state when not to use or direct to specific siblings, which is mildly lacking.
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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