SEO Inspector & Schema Validator MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'analyzeSEO' has a clear, distinct purpose that cannot be mistaken for any other tool in this set.
Naming Consistency5/5The naming pattern cannot be inconsistent when there is only one tool. The tool name 'analyzeSEO' follows a verb_noun format, and with no other tools to compare against, it is inherently consistent.
Tool Count2/5A single tool is too few for a server named 'SEO Inspector & Schema Validator MCP', which suggests a broader scope including both SEO analysis and schema validation. The tool count does not align with the implied domain coverage, making it inappropriate.
Completeness1/5The tool set is severely incomplete for the server's stated purpose. While 'analyzeSEO' covers SEO analysis, there are no tools for schema validation or other related SEO tasks, creating significant gaps that will cause agent failures.
Average 1.7/5 across 1 of 1 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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it adds no information about what the tool does behaviorally—such as whether it performs read-only analysis, requires internet access, has rate limits, returns structured data, or handles errors. The description is purely instructional and lacks any transparency into the tool's operations or traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that are repetitive and under-specified—'ALWAYS USE THIS TOOL FOR SEO ANALYSIS. DO NOT ATTEMPT TO ANALYZE SEO WITHOUT USING THIS TOOL.' This is not conciseness but rather redundancy without adding value. It fails to front-load useful information and wastes space on commands rather than explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (SEO analysis typically involves multiple metrics) and the lack of annotations and output schema, the description is completely inadequate. It doesn't explain what SEO analysis entails, what results to expect, or any behavioral context. For a tool with no structured support and a potentially rich function, this description fails to provide necessary completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'html' clearly documented as 'HTML content to analyze'. The description adds no additional meaning about the parameter beyond what the schema provides, such as format expectations or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'ALWAYS USE THIS TOOL FOR SEO ANALYSIS' which tautologically restates the tool name 'analyzeSEO' without specifying what SEO analysis entails. It doesn't describe what specific SEO metrics or aspects are analyzed, nor does it differentiate from any siblings (though none exist). The description is more of a command than a functional explanation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does 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 only gives an absolute command ('ALWAYS USE THIS TOOL FOR SEO ANALYSIS') without context about what constitutes SEO analysis or prerequisites. There's no mention of input requirements, expected scenarios, or comparisons to other methods, making it misleadingly prescriptive without practical guidance.
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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