seo-audit-mcp
seo-audit-mcp
Ein MCP-Server, der Claude (oder jedem MCP-Client) die Möglichkeit gibt, das technische SEO einer Live-Website zu prüfen: Sitemap-Abdeckung, Probleme pro Seite und Weiterleitungsketten.
Frag in einfacher Sprache – „auditiere mortgagecalculatortools.com und sag mir, von welchen Seiten Google nie erfährt" – und das Modell ruft die Tools auf, durchsucht die Website und antwortet mit Details.
Das Problem, das es löst
Über die sitemap.xml einer Website teilst du Google mit, welche Seiten existieren. Wenn eine Seite
darin fehlt, gibt es keinen Fehler und keine Warnung – die Seite sammelt einfach nie
Impressionen. Die manuelle Prüfung bedeutet, ein Dateisystem-Verzeichnis gegen eine XML-Datei
zu diffen, daher macht das in der Praxis niemand.
Fallstudie: Eine Lücke von 25 Seiten, die sich als richtig erwies
Die erste Website, auf die das Tool gerichtet wurde, hatte 125 HTML-Dateien auf der Festplatte und 100 URLs in ihrer Sitemap. Eine Lücke von 25 Seiten – die Art von Befund, der als Bug aufgeschrieben und jemandem zugewiesen wird.
Ein sitemap_coverage-Aufruf deckte die Lücke auf, und ein audit_urls-Aufruf an einer
Stichprobe erklärte sie: Jede der 25 Seiten trug <meta name="robots" content="noindex, follow">. Es handelte sich um zwei bewusst von der Indizierung ausgenommene Inhaltscluster,
und die Sitemap hatte genau richtig gehandelt, sie wegzulassen. Anschließend gegen
das Dateisystem verifiziert: 25 noindex-Seiten auf der Festplatte, dieselben 25 fehlen in
der Sitemap, null noindex-Seiten fälschlich enthalten. Perfekte Konsistenz.
Das ist das nützliche Ergebnis. Eine reine Abdeckungszahl („125 vs. 100") wirkt wie ein
Defekt und kostet jemanden einen ganzen Tag; Abdeckung plus noindex-Status pro Seite
beantwortet die Frage in einer Minute. Dieses Tool ist genauso wertvoll für die
Fehlalarme, die es verhindert, wie für die echten Lücken, die es findet – deshalb
meldet audit_urls noindex pro Seite und zählt nicht nur URLs.
Related MCP server: web-audit-mcp
Tools
Tool | Was es tut |
| Ruft |
| Durchsucht URLs parallel und meldet Probleme pro Seite: fehlerhafter Status, Weiterleitungsketten, fehlendes/zu langes |
| Vergleicht eine Sitemap mit einer Liste von URLs, von denen du weißt, dass sie existieren → was in der Sitemap fehlt, was deklariert, aber tot ist. |
| Verfolgt Weiterleitungsketten, markiert Mehrsprung-Ketten und Ketten, die in 4xx/5xx enden – nach einer Änderung der URL-Struktur verwenden. |
Jedes Tool gibt strukturiertes JSON mit einer issues-Liste pro Seite und einer
aggregierten issue_summary zurück, sodass das Modell über Zahlen nachdenken kann, statt
rohes HTML erneut zu lesen.
Installation
pip install -e .Erfordert Python 3.10+. Abhängigkeiten: mcp>=2.0.0, httpx.
Verbinde es mit Claude Code
Füge es zu .mcp.json in deinem Projekt hinzu (oder ~/.claude.json für die globale Nutzung):
{
"mcpServers": {
"seo-audit": {
"command": "python",
"args": ["-m", "seo_audit_mcp"]
}
}
}Für Claude Desktop kommt derselbe Block in claude_desktop_config.json.
Dann frag einfach:
Rufe die Sitemap für https://example.com/sitemap.xml ab, prüfe die ersten 20 URLs und fasse die Probleme nach Häufigkeit zusammen.
Direkt ausführen
python -m seo_audit_mcp # stdio transportDesign-Anmerkungen
Drei Entscheidungen, die hervorzuheben sind, weil sie den Unterschied zwischen einer Demo und etwas ausmachen, das man auf der Produktionswebsite eines Kunden einsetzen kann:
Das Crawling ist konstruktionsbedingt ratenbegrenzt. fetch_many läuft hinter einem
asyncio.Semaphore, das auf 16 gleichzeitige Anfragen begrenzt ist, und jedes Tool begrenzt
seine Eingaben. Eine Sitemap mit 500 URLs würde ohne diese Obergrenze 500 Sockets auf
einmal öffnen und wie ein Angriff auf den Zielhost wirken. Das Crawling ist eine Last, die die Ziel-
Website trägt, daher ist die Obergrenze von der Tool-Oberfläche aus nicht nach oben konfigurierbar.
Kein Abruffehler bricht einen Lauf ab. fetch_one fängt httpx.HTTPError ab und
zeichnet ihn auf dem zurückgegebenen PageAudit auf, anstatt ihn auszulösen. Ein toter Host in einem
Crawl mit 200 URLs verschlechtert eine Zeile, anstatt 199 gute Ergebnisse zu verlieren.
Das Parsing ist bewusst tolerant. Echtes HTML ist häufig fehlerhaft genug,
dass ein strenger Parser, der mitten im Crawl eine Ausnahme auslöst, ein Risiko darstellt. Die Extraktoren sind
großzügige Regexe, die None zurückgeben, anstatt zu werfen – aber mit behobenen Fallstricken:
<script>- und <style>-Inhalte werden vor der Wortzählung und Heading-Extraktion entfernt, sodass ein <h1> in einem JS-String-Literal nicht als Überschrift
gezählt wird, und relative Canonicals werden gegen die Seiten-URL aufgelöst.
normalize_url entfernt bewusst keine abschließenden Schrägstriche: /a und /a/
können tatsächlich verschiedene Seiten sein, und ihre Zusammenlegung würde Duplicate-Content-Probleme
verbergen, die dieses Tool aufdecken soll.
Tests
pip install -e ".[dev]"
pytestDie Suite ist netzwerkfrei – HTTP wird über httpx.MockTransport getestet,
sodass sie in CI und im Flugzeug läuft. Sie deckt die Parsing-Grenzfälle ab, die in der
Produktion beißen: in Skripten eingebettete Überschriften, Sitemaps ohne Namespace, relative
Canonicals, Sitemap-URLs, die ein gestyltes HTML-404 mit einem 200-Status zurückgeben, und
Nicht-HTML-Inhaltstypen, die fälschlich als Seiten „ohne Titel" gemeldet werden.
Lizenz
MIT
Available Tools
4 toolsaudit_urlsA
Crawl a list of URLs and report per-page technical SEO issues: broken status codes, redirect chains, missing or over-length titles and meta descriptions, missing or duplicate H1, missing canonical, noindex, and thin content. Returns a per-URL breakdown plus an issue summary.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | ||
| concurrency | No | ||
| timeout_seconds | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It usefully states that the tool crawls URLs and returns a per-URL breakdown plus an issue summary, but it does not disclose operational behaviors such as crawl duration, rate limiting, redirect-following details, or auth/network requirements. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single information-dense sentence that front-loads the action and resource, then lists issue categories and the return shape. It avoids repetition and wastes no words, though the enumeration makes it slightly dense.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so the description does not need to detail return values, and it does give a useful high-level summary. However, with zero annotations, zero schema descriptions, and no usage guidance, the description leaves concurrency/timeout semantics and tool-selection boundaries undocumented, making it only moderately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it never names or explains urls, concurrency, or timeout_seconds. The parameter names are somewhat self-explanatory, yet the description adds no detail about what concurrency or timeout_seconds control, how URLs should be formatted, or whether limits apply.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb-resource pair ('Crawl a list of URLs and report per-page technical SEO issues') and then enumerates the exact issue categories. This clearly distinguishes it from siblings like fetch_sitemap and sitemap_coverage by centering on per-URL technical SEO auditing, even though it overlaps with check_redirects on redirect chains.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies its use case by listing SEO checks, but it never explicitly states when to prefer this over check_redirects, fetch_sitemap, or sitemap_coverage, nor does it give any 'when not to use' guidance. An agent can infer the purpose but must decide on selection criteria without direct help.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_redirectsA
Trace the redirect chain for each URL and flag chains longer than one hop, redirect loops, and URLs that resolve to a 4xx/5xx. Use after a site migration or a URL-structure change.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It clearly discloses what the tool does: traces chains, flags one-hop violations, loops, and error responses. It could add operational details like network cost or rate limits, but the core behavior is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler, and the main behavior is front-loaded. The second sentence supplies a practical trigger for use. Every part earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema present, the description covers what the tool does and when to use it. It does not fully cover input format details, but the missing information is minor given the low complexity and available output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds little about the 'urls' parameter beyond saying 'for each URL.' It does not clarify expected URL format (absolute vs relative), whether schemes are required, or any limits on list length, so the agent gets almost no parameter guidance beyond the bare schema type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Trace the redirect chain for each URL' and names concrete detection outcomes (long chains, loops, 4xx/5xx). This is distinct from siblings like fetch_sitemap or audit_urls, making the tool's purpose immediately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use after a site migration or a URL-structure change,' which gives a clear context for when this tool is appropriate. It does not mention alternatives or when not to use it, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_sitemapA
Fetch and parse a sitemap.xml, following sitemap-index nesting, and return every page URL it declares. Call this first when auditing a site you do not have a URL list for.
| Name | Required | Description | Default |
|---|---|---|---|
| sitemap_url | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It discloses that the tool follows sitemap-index nesting and returns every declared page URL, which are meaningful behavioral details beyond what the schema shows. It does not mention failure modes or network behavior, but for a straightforward fetch-and-parse read operation this is reasonably transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence states what the tool does and the second gives usage guidance. The most important behavioral details are front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with an output schema present, the description is complete: it explains what the tool does, how it behaves with index nesting, what it returns, and when to call it. Nothing essential for correct invocation is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It indirectly clarifies that sitemap_url should point to a sitemap.xml and that index nesting is followed, but it does not explicitly describe the URL format, required scheme, or example values. Because the single parameter is highly self-evident from the tool name and description, this is adequate but not enriched.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action: 'Fetch and parse a sitemap.xml' and the specific outcome: 'return every page URL it declares.' It also mentions the non-obvious behavior of following sitemap-index nesting, which distinguishes this from simply fetching one XML file. The phrase 'Call this first when auditing a site' also separates it from siblings like audit_urls and sitemap_coverage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides explicit guidance: 'Call this first when auditing a site you do not have a URL list for.' This clearly tells an agent when to use it. However, it does not name alternative tools or explicitly state when not to use it, so it falls just short of full usage differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sitemap_coverageA
Compare a sitemap against a list of URLs you know exist (e.g. from the filesystem or a crawl) and report which are missing from the sitemap and which the sitemap declares but are unreachable. Missing pages are pages Google is never told about.
| Name | Required | Description | Default |
|---|---|---|---|
| verify | No | ||
| known_urls | Yes | ||
| sitemap_url | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose that the tool checks reachability and reports missing/unreachable URLs, which is meaningful. However, it does not explain the 'verify' behavior, whether network requests are made to each known URL, or any side effects such as rate-limit impact or request costs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences cover the operation, inputs, outputs, and practical significance with no filler. The core comparison is front-loaded, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so describing return values is not necessary. The description is adequate for a straightforward comparison tool, but it omits the semantics of the optional 'verify' parameter and does not provide guidance on how this tool relates to siblings such as audit_urls or check_redirects, which would help an agent choose correctly in more ambiguous cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It adds some meaning by indicating 'sitemap' maps to sitemap_url and 'list of URLs you know exist' maps to known_urls. However, the 'verify' parameter is entirely unexplained despite being a schema property with a default value, and the mapping from description to parameters remains implicit rather than explicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('compare') with clear resources: a sitemap and a list of known URLs. It precisely defines the two reported outcomes—URLs missing from the sitemap and sitemap entries that are unreachable—and the final sentence explains why this matters. It is clearly distinct from siblings like fetch_sitemap or audit_urls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives a clear scenario for when to use the tool: when you have a sitemap and a separate list of URLs known to exist, such as from a filesystem or crawl. It does not explicitly name alternatives or exclusion conditions, but the context is sufficient for an agent to identify this as the coverage-comparison tool among the siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
4 tool updates
v1.0.0- First observed
audit_urls - First observed
check_redirects - First observed
fetch_sitemap - First observed
sitemap_coverage
TDQS
Scored across 4 tools
Each tool targets a distinct phase of an SEO audit: sitemap fetching, page-level auditing, sitemap coverage comparison, and redirect tracing. The main overlap is that audit_urls already reports redirect chains and broken status codes, which overlaps with check_redirects.
Three tools follow a clear verb_noun pattern (fetch_sitemap, audit_urls, check_redirects), but sitemap_coverage is a noun_noun exception. The inconsistent name is still readable and does not create real confusion.
Four tools is a well-scoped size for a focused SEO audit server. Each tool has a clear job, and there is no redundant filler or overwhelming number of endpoints.
The server covers sitemap parsing, on-page/technical issue auditing, sitemap coverage, and redirects, but it lacks a site-crawling or internal-link-discovery tool, which is needed to find URLs not listed in a sitemap. This is a notable gap for a full audit, though the core workflow is usable with an existing URL list.
Maintenance
Related MCP Connectors
- CrawlieOAuthapp.crawlie
Technical SEO + GEO (AI-search) site audits: hosted crawls, prioritized fixes, report diffs.
Audit public webpages and supplied markup for HTML, CSS, SEO, JSON-LD, and link issues.
Audit any site's AI visibility from your assistant: crawler access, rendering, and schema.
- seegeoOAuthcom.see-geo
Audit any website for AI visibility: graded report, findings with fixes, AI crawler access check.
Related MCP Servers
- AlicenseAqualityDmaintenanceEnables SEO auditing and site analysis by crawling websites, identifying issues, and generating reports like sitemaps and markdown exports.59 npm4MIT
- FlicenseNot gradedqualityDmaintenanceEnables auditing of websites for performance, SEO, accessibility, security, and mobile readiness, with tools to validate URLs, run page audits, save results, and retrieve reports.1-
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to crawl and audit websites for SEO issues, returning structured JSON reports with errors, warnings, and key statistics.MIT
- FlicenseNot gradedqualityDmaintenanceAudits any website for SEO issues, providing scored health checks, schema validation, and performance analysis through AI assistants.-