atomno-mcp-seo-audit

Official

by atomno-mcp

Overview Schema Related Servers Score Discussions

Python

Remote

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.4.7
Disambiguation5/5
Each tool targets a distinct aspect of SEO: full audit, diff comparison, sitemap/robots checks, meta/schema generation, and issue explanation. No overlapping purposes.
Naming Consistency5/5
All tools follow a clear verb_noun pattern in snake_case (e.g., audit_site, check_sitemap, build_meta), making them predictable and easy to distinguish.
Tool Count5/5
With 8 tools, the server covers core SEO audit functionality without being overwhelming or too sparse. Each tool earns its place.
Completeness4/5
The tool set covers the main lifecycle of SEO auditing: running audits, checking specific files, generating meta/schema, and explaining issues. Minor gaps like page speed or mobile checks exist but are not essential for the stated purpose.
Average 4.3/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 17 commits in the last 12 weeks
- Last stable release on July 14, 2026
- 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
This repository includes a glama.json configuration file.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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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, the description covers the generative and validation behavior, but does not mention side effects, prerequisites, or error handling.
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 concise sentences with all essential information, no superfluous content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a simple generation tool with output schema; it explains purpose, validation, and output.
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?
Adds value beyond the schema by specifying length checks (title 50–60, description 120–160) and that it returns a ready block; schema already covers field keys and lang options.
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 generates <head> meta tags (title, description, canonical, Open Graph, Twitter Card) and validates lengths, distinguishing it from sibling tools like build_jsonld or audit_site.
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 when or when-not guidance is provided; usage context is implied but not delineated against alternatives.
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 full burden. It discloses the read-only nature (listing) and the parameter but does not mention any behavioral traits such as rate limits, authentication, or data freshness. For a simple list, it is adequate but not fully transparent.
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 concise sentences, each adding value: first states the action, second explains the benefit. No unnecessary words or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema, the description does not need to explain return values. It fully covers the tool's purpose and scope with one optional parameter, making it complete for an agent to understand.
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 full coverage (100%) for the single parameter 'lang', with a clear description and default value. The tool description does not add any extra parameter information beyond what the schema already provides, so 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 clearly states it lists all engine checks with free/PRO breakdown by categories. It specifies the purpose of understanding which checks are free vs PRO and which categories are covered (security, SEO, etc.). This distinguishes it from sibling tools like audit_site or check_sitemap.
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 context on when to use this tool: to understand free/PRO coverage and categories. However, it does not explicitly state when not to use it or mention alternative tools for specific needs.
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?
Annotations are absent, so the description carries full burden. It discloses that validation is local, no fetch is executed, and lists specific checks. No contradictions. It does not mention auth needs or rate limits, but those are irrelevant for a local validation tool.
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 waste. First sentence lists all checks performed; second clarifies operational details. Front-loaded with purpose.
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 and presence of output schema, the description covers the key aspects: what is checked, that it's local, and parameters. Slightly lacks integration with sibling differentiation, but adequate.
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%; both parameters are described. The description adds 'вставьте текст целиком' (paste the entire text) for the content parameter, which adds clarity beyond the schema. Baseline 3 with slight improvement.
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 states the tool validates robots.txt content for syntax, sitemap directive, blocking CSS/JS from render bots, and explicit disallowances for AI crawlers. It clearly identifies the resource (robots.txt) and the action (validate), and distinguishes from siblings like check_sitemap or audit_site.
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 says to pass the file text and emphasizes that no fetch is performed; validation is local. This implies when to use (when you have raw content). It does not explicitly list when not to use or name alternatives, but the sibling tools provide context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and discloses key behaviors: stateful operation, need for API key, PRO function status, and the delta comparison process. No contradictions.
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 concise and front-loaded with the main purpose. It could be slightly more structured, but it efficiently conveys essential details without verbosity.
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 existence of an output schema, the description adequately covers purpose, statefulness, and prerequisites. It lacks explicit mention of storage limits or comparison scope, but overall is complete for the tool's complexity.
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%, providing complete parameter documentation. The tool description adds no additional semantics beyond what the schema already provides, warranting the baseline score 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?
The description clearly states the tool compares a site with a past audit to show improvements and degradations. It distinguishes itself from sibling tool 'audit_site' by emphasizing temporal tracking as a unique capability.
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 when to use the tool (tracking changes over time) and mentions prerequisites (API key, baseline creation on first call). However, it lacks explicit guidance on when not to use it or direct alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses behavior: real HTTP requests, deterministic health score, free tier without key. It details what's checked and the return format, providing transparency about the tool's operation and side effects.
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 well-structured with a clear summary first, then details. It is detailed but not overly verbose. Every sentence adds value, though it could be slightly more 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 tool's complexity (multiple checks, health score, PRO features), the description covers essential aspects: real HTTP requests, deterministic output, free tier, and outputs. It mentions PRO geo sub-score but doesn't detail PRO fully. With an output schema present, completeness is adequate.
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 baseline is 3. The description adds minimal extra context beyond schema (e.g., explaining depth values in prose). It does not add significant parameter-level meaning that the schema lacks.
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 performs a technical SEO audit with real HTTP requests, listing specific checks (SSL, security headers, redirects, etc.) and outputs like health score, problems list. It distinguishes itself from sibling tools like audit_diff or check_sitemap by being the comprehensive audit 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 implies when to use (to get factual data the model can't guess) and mentions free tier. However, it lacks explicit when-not-to-use or direct comparison with alternatives like check_sitemap or validate_robots, which slightly reduces clarity for an agent deciding between tools.
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 describes the tool's behavior: downloading, parsing, checking for specific issues, and SSRF protection. It does not mention authentication or error handling but is fairly comprehensive for a sitemap checker.
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 action, no wasted words. The first sentence packs essential details, the second adds a relevant security note.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params) and presence of an output schema, the description covers all necessary aspects: download, parse, check issues. No critical gaps identified.
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% with both parameters described. The description adds context beyond the schema by explaining what the tool does with the URL and providing an example format. This adds meaningful value.
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 downloads and parses a sitemap, identifies format and URL count, and checks for common issues like 404, non-XML, http links, missing lastmod, and exceeding 50,000 limit. It also mentions SSRF protection. This distinguishes it from sibling tools like validate_robots.
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 checking sitemaps but does not explicitly state when to use versus alternatives or provide exclusions. Context is clear but lacks explicit guidance.
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, description carries full burden. It discloses that title/category are localized, advice currently in Russian (advice_lang field), and behavior for unknown id (found=false). This is adequate behavioral context.
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 sentences, front-loaded with main purpose, no wasted words. Each sentence adds distinct information: purpose, input source, behavior details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present (context signal), description doesn't need return values. It covers input, localization, error case. Complete for a simple explanation 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 coverage is 100%, so baseline 3. Description adds that check_id comes from audit_site/list_checks and gives example 'hsts', but lang parameter is already well-specified in schema. Minor added value.
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 it explains one check (why important and how to fix), specifies input source (check_id from audit_site/list_checks), and distinguishes from siblings like list_checks which lists checks.
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?
Explicitly says when to use (after audit_site or list_checks) and what check_id to provide. Does not mention alternatives but purpose is narrow enough that no further guidance is needed.
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, the description carries the burden. It discloses that it returns a <script> tag, validates required fields, and avoids data invention. This sufficiently reveals core 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 two concise sentences. The first sentence states the purpose, the second adds behavioral constraints and output format. No wasted words.
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 output schema exists externally, the description covers generation behavior, validation, and output format adequately. It lacks error details but is complete for typical usage.
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%, but the description adds value by explaining special field structures for FAQPage (faq array) and BreadcrumbList (items array), enhancing understanding beyond schema 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 generates a ready JSON-LD schema.org block from passed fields, distinguishing it from siblings like build_meta or check_sitemap which handle different metadata tasks.
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 that the tool only uses passed data, doesn't invent, and hints about missing required/recommended fields. It doesn't explicitly state when not to use it, but the context is clear.
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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