SEO Review Tools - MCP server

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by SEO-Review-Tools

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose targeting specific SEO metrics or data types, such as authority scores, backlinks, keyword statistics, keyword suggestions, SEO content scores, SERP data, and website traffic (both individual and bulk). There is no overlap or ambiguity in what each tool retrieves or posts.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with 'get_' or 'post_' prefixes, using snake_case uniformly. This predictable naming scheme makes it easy for agents to understand and select the appropriate tool based on the action and resource.
Tool Count5/5
With 8 tools, the server is well-scoped for SEO analysis, covering key aspects like authority, backlinks, keywords, content, SERP, and traffic. Each tool earns its place without feeling excessive or insufficient for the domain.
Completeness4/5
The tool set provides comprehensive read coverage for SEO metrics, including a bulk post operation for traffic data. A minor gap exists in update or delete capabilities, but this is typical for data retrieval APIs, and agents can work around it effectively.
Average 2.9/5 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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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

Behavior2/5
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. It only states the action ('Get') without details on rate limits, authentication needs, response format, or potential side effects. This is inadequate for a tool with 5 required parameters and no output schema.
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 with zero waste. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 required parameters) and lack of annotations or output schema, the description is incomplete. It does not explain what the SEO content score entails, how it is calculated, or what the return values look like, leaving significant gaps for agent understanding.
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 fully documents all 5 parameters. The description adds no additional meaning about parameters, such as format examples or usage context, but the schema provides sufficient baseline information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'Get[s] the SEO content score from SEO Review Tools', which provides a clear verb ('Get') and resource ('SEO content score'). However, it lacks specificity about what the score measures or how it differs from sibling tools like get_authority_scores or get_keyword_statistics, making it somewhat vague.
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 is provided on when to use this tool versus alternatives. The description does not mention context, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action but lacks details on permissions, rate limits, response format, or error handling. For a tool that likely involves external API calls or data retrieval, this omission is significant, though it doesn't contradict any annotations.
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, direct sentence with no wasted words, front-loading the core action and resource. It efficiently communicates the essential purpose without unnecessary elaboration, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of SEO tools and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'authority scores' are, how results are returned, or any behavioral traits, leaving gaps that could hinder effective tool selection and invocation by an AI 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 description coverage is 100%, so the input schema already documents both parameters ('urls' and 'metrics') clearly. The description adds no additional meaning beyond what's in the schema, such as explaining what 'authority scores' entail or how metrics like 'pa|da' are interpreted, meeting the baseline for adequate but not enhanced parameter context.
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 verb ('Get') and resource ('authority scores for a list of URLs'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'get_website_traffic' or 'get_seo_content_score', which might also involve URL analysis but for different metrics, leaving some ambiguity about its specific niche.
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 alternatives. It doesn't mention sibling tools like 'get_website_traffic' or 'get_seo_content_score', nor does it specify prerequisites, contexts, or exclusions, leaving the agent to infer usage based on tool names alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic action without details on permissions, rate limits, response format, or potential side effects. This is inadequate for a tool that likely involves external API calls and data retrieval.
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 with zero wasted words, making it easy to parse and understand quickly. It's appropriately sized for its purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of keyword statistics tools, no annotations, and no output schema, the description is insufficient. It lacks details on what statistics are returned, how results are structured, or any behavioral traits, leaving significant gaps for agent understanding.
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 documents all parameters (keywords, hl, location) adequately. The description adds no additional meaning beyond what the schema provides, such as examples or constraints, resulting in a baseline score.
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 action ('Get') and resource ('keyword statistics from SEO Review Tools'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_keyword_suggestions' or 'get_serp', which might also involve keyword-related data, so it doesn't reach the highest 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 alternatives like 'get_keyword_suggestions' or 'get_serp'. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent with minimal direction for tool selection.
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 carries the full burden of behavioral disclosure. It mentions the API source but lacks details on rate limits, authentication needs, error handling, or the format of returned suggestions. For a tool with no annotations, this leaves significant gaps in understanding how it behaves operationally.
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 directly states the tool's purpose without any unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly while conveying the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for effective tool use. It doesn't explain what the suggestions include (e.g., related keywords, volumes), how results are structured, or any behavioral traits like pagination. For a tool with three parameters and no structured output, more context is needed to guide an agent fully.
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 has 100% description coverage, clearly documenting all three parameters. The description adds no additional meaning beyond what the schema provides, such as examples or usage tips. According to the rules, with high schema coverage, the baseline score is 3, as the schema adequately handles parameter documentation.
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 action ('Get keyword suggestions') and the source ('from SEO Review Tools API'), which is specific and unambiguous. However, it doesn't explicitly differentiate this tool from sibling tools like 'get_keyword_statistics' or 'get_seo_content_score', which might also relate to keywords or SEO analysis, leaving some room for confusion about its unique role.
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 alternatives. With sibling tools such as 'get_keyword_statistics' and 'get_seo_content_score' available, there is no indication of scenarios, prerequisites, or exclusions that would help an agent choose appropriately, relying solely on the tool name for inference.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves SERP data but fails to describe traits like whether it's read-only, requires authentication, has rate limits, or what the output format entails. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence with zero waste, front-loading the core purpose. It is appropriately sized for a simple tool, making it easy to parse without unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no output schema, no annotations), the description is incomplete. It lacks details on behavioral traits, output format, and usage context, failing to compensate for the absence of structured data, which is inadequate for effective tool invocation.
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 has 100% description coverage, clearly documenting the three required parameters (keyword, hl, location). The description adds no additional meaning beyond this, such as examples or constraints, so it meets the baseline score of 3 where the schema does the heavy lifting.
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 action ('Get SERP data') and the source ('from SEO Review Tools'), providing a specific verb and resource. However, it doesn't explicitly differentiate this tool from its siblings like 'get_keyword_statistics' or 'get_website_traffic', which might also involve search-related data, leaving some ambiguity about its unique scope.
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 offers no guidance on when to use this tool versus alternatives, such as its siblings. It lacks context on scenarios where SERP data is needed over other SEO metrics, prerequisites, or exclusions, leaving the agent without usage direction.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does ('Get website traffic data') without any context on permissions, rate limits, data freshness, or response format. This is a significant gap for a tool that likely involves external API calls and data retrieval.
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 with zero waste. It's front-loaded with the core purpose and doesn't include unnecessary details, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of fetching external SEO data, no annotations, and no output schema, the description is incomplete. It lacks crucial details like what traffic data is returned (e.g., visits, sources), error handling, or dependencies on external services, leaving the agent under-informed for effective tool use.
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 description coverage is 100%, with the single parameter 'url' clearly documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides, such as URL format requirements or examples. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
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 verb 'Get' and the resource 'website traffic data from SEO Review Tools', making the purpose understandable. However, it doesn't explicitly differentiate from siblings like 'get_authority_scores' or 'get_backlinks', which might also retrieve data from the same source but for different metrics.
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 alternatives. It doesn't mention when to choose this over other sibling tools (e.g., 'get_authority_scores' for authority metrics) or 'post_bulk_website_traffic' for bulk operations, leaving the agent to infer usage from tool names alone.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool posts data to an API but doesn't clarify whether this is a write operation, what permissions or authentication might be required, potential side effects (e.g., data overwriting), rate limits, or error handling. This leaves significant gaps for a tool that likely mutates 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?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a tool that likely performs a write operation. It doesn't address behavioral aspects like authentication needs, data persistence, or response format, which are critical for an agent to use the tool correctly and safely.
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 fully documents the three parameters (domains, location, hl). The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between parameters or usage nuances. This meets the baseline for high schema coverage.
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 action ('Post bulk website traffic data') and target ('SEO Review Tools API'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_website_traffic', which appears to retrieve rather than submit data, leaving some ambiguity about when to use each.
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 alternatives, such as the sibling 'get_website_traffic'. It lacks context about prerequisites, use cases, or exclusions, leaving the agent to infer usage based on the tool name alone.
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 carries the full burden of behavioral disclosure. It states what the tool does but lacks details on traits like rate limits, authentication needs, response format, or potential errors. For a data retrieval tool with no annotation coverage, this is a significant gap in 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?
The description is a single, efficient sentence: 'Get backlinks for a specified URL.' It is front-loaded with the core purpose, has zero waste, and is appropriately sized for a simple tool. Every word earns its place by conveying essential information without redundancy.
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 low complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks context on usage, behavioral traits, or output details. Without annotations or an output schema, the description should do more to compensate, but it only meets the bare minimum for a read-only data 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?
Schema description coverage is 100%, meaning the input schema fully documents both parameters ('url' and 'unique_domains'). The description adds no additional meaning beyond what the schema provides, such as explaining the significance of backlinks or how 'unique_domains' affects results. Baseline 3 is appropriate when the schema handles parameter documentation.
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: 'Get backlinks for a specified URL.' It specifies the verb ('Get') and resource ('backlinks'), and the scope ('for a specified URL') is unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_authority_scores' or 'get_serp', which might also relate to SEO data but serve different purposes.
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 alternatives. It doesn't mention sibling tools or contexts where this tool is preferred, such as for SEO analysis versus other metrics. Without any usage context or exclusions, the agent must infer when this tool is appropriate based on the name alone.
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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