What Waild MCP does: an AI SEO tool that lives inside Claude
Most AI SEO tools are one of two things: a dashboard you visit, or a language model you hope is right. Waild MCP is neither. It is an MCP server — a set of tools Claude can call mid-conversation — that connects the AI assistant you already write with to live search data: keyword research, SERP analysis, competitor and backlink intelligence, on-page audits, content briefs, and a grading gate that measures every draft against the pages that actually rank in Google search results. The premise is simple: AI content only helps your SEO when it is based on what search engines currently reward, and only an AI tool with live data can know that. This page walks the whole surface, grouped by the five jobs it does, with the genuine numbers from recorded sessions at each step — so you can judge the tool by its outputs, not its adjectives. (New to the protocol itself? Start with what is an MCP server?)
Research: size the market before writing a word
Every SEO strategy starts with search volume data and search intent. Ask Claude whether a market exists
and it calls keyword_overview, which returns each keyword's monthly volume,
cost-per-click, intent classification, and keyword difficulty in one compact row. In the recorded
research session, a founder asks about AI note-taking apps and gets the shape of the market in
one call: “note taking app” draws 22,200 US searches a month at difficulty 49 — too hard for a
new domain — while “best ai note taking app” brings 2,400 searches at difficulty 12, a fight a
new site can win. That is the core of AI-assisted keyword research: many candidate keywords in,
relevant and winnable ones out, minutes instead of a day in spreadsheets.
A second call, serp_question_map, harvests what searchers actually ask around a
keyword: the People-Also-Ask tree, related searches, and long-tail questions, each tagged with
intent and a suggested role (FAQ entry or H2). In the same session it surfaced “Is an AI note
taker legal?” and “What is the safest AI notetaker?” — privacy questions no ranking page owned —
plus the fact that no featured snippet was live on that SERP. That is a content angle and a
position-zero opportunity, found in seconds.
The research layer runs deeper when you need it: keyword suggestions and related keywords,
SERP-overlap clustering (cluster_keywords_by_serp) that decides whether a set of
keywords needs one page or several, topical maps (topic_cluster_plan) that lay out
pillar-and-spoke architectures with internal links, Google Trends, and search-intent
classification for any keyword list. We used it on this very page: before writing, we pulled the
keyword “ai seo tools” — 2,400 US searches a month, difficulty 10, commercial intent. Real
research, eating its own cooking.
Write: a content brief from the live SERP, not from memory
The writing layer is grounded in the first rule of SEO content: never draft from imagination. The
build_content_brief tool reads the top-ranking pages for a keyword and compresses
them into a writer-ready brief — search intent and funnel stage, the word-count band of the
pages that rank (25th percentile, median, 75th), an outline that separates must-cover sections
from differentiators, term targets with recommended usage counts, the questions to answer, meta
title options, and a featured-snippet instruction when position zero is open.
The recorded session shows the brief for “best ai note taking app”: a 3,011-word median target (competitors range 2,420 to 3,787), 50 terms with counts, the sections every winner covers, and a concrete snippet play — “no featured snippet is live yet; put a keyword-matching H2 above a numbered list.” Claude drafts against that brief inside the same conversation. No copy-pasting between a research tab, a spreadsheet, and a writing app; the content workflow is the chat.
Two more tools back the drafting stage. term_coverage_target extracts the shared
vocabulary of the ranking pages — the terms Google's winners all use — with per-term counts and
heading placement. And competitor_content_teardown reverse-engineers a specific
ranking URL into a match-and-beat outline: its structure, term consensus, and the subtopics it
misses. The shape of the consensus, never the wording.
Grade: the quality gate that refuses slop
This is the part other AI SEO software does not have, and it is the reason AI content from this
workflow ranks better than AI content from a bare chat window. Every draft goes through
grade_draft, which scores it 0–100 against the live SERP: term coverage with
per-term status, word count measured against the competitor band, readability, and a
prose-quality report that counts AI-tell phrases per 1,000 words and flags structural tells like
over-signposting.
The recorded session shows what happens to a lazy draft. A 119-word first attempt at “best ai note taking app” scored 16 out of 100: 37 missing terms, word count measured against a 3,011-word bar, 42 AI-cliché hits per 1,000 words, and “in conclusion,” “cutting-edge,” and “unlock” flagged by name. It did not ship. The loop — rewrite, re-grade, repeat — continues until the draft clears the target score and reads like a person wrote it. For comparison, our own MCP explainer guide went through the same gate and shipped at 86/100 with 0.5 AI-tells per 1,000 words.
Above grading sits ship_check, a single readiness verdict that checks structure,
coverage, information gain, prose quality, and fact verification, and returns the exact next
action for every failed gate. There is also humanize_draft, which runs several
fresh rewrites through a judge panel server-side and returns only the winner — a tournament, not
a coin flip — and audit_draft_url, which grades a live or staged page the same way
before you publish, without leaving the chat. The point of all of it: optimization scores alone
do not decide. Content that is thin, off-intent, mechanically optimized without substance, or
template-flavored gets bounced regardless.
Maintain: the cheapest rankings you will ever get
Every fact the server fetches lands in a shared cache, and the maintenance tools read from it at
no API cost. quick_wins finds your pages sitting in positions 4–20 — one edit from
page one — and ranks them by expected return, each with a concrete lever.
content_decay_scan watches ranking history accrue and queues pages that are losing
ground, with the right action for each: refresh, consolidate, or redirect.
detect_cannibalization catches keywords where two of your URLs split equity and
names the keeper. internal_link_suggestions proposes specific links from your
related pages into a target page, with varied anchor text.
Maintenance is the least glamorous part of SEO and often the highest-return one: refreshing an existing page frequently outperforms publishing a new one, and these tools make that the default move rather than an afterthought. Because they run over cached data, re-checking costs nothing — a maintenance sweep is free after the research that built the cache.
Compete: know the fight before you pick it
The competitive layer answers the question founders actually ask: should I even try?
In the recorded session, “Notion dominates this space — how strong are they really?” gets two
calls. domain_rank_overview measures the brand's organic visibility: 656 US
keywords worth roughly 144,000 monthly organic
visits. backlinks_summary: 11.1 million backlinks across 126,359 referring domains.
The honest conclusion follows — you do not beat that head-on, you route around it through
long-tail keywords the incumbent cannot own.
Routing around is what keyword_winnability_score is for. Before any article gets
written, it compares the real link bar of the top-ten pages against your domain's authority and
returns a verdict: winnable, stretch, or unrealistic. The rest of the layer fills in the map —
competitor domain discovery, keyword intersections (terms they rank for and you don't),
content_gap_roadmap for a prioritized list of pages to build or upgrade, full
backlink profiles with anchors and gap analysis, and on-page audits of any URL.
Where it fits in your SEO stack
A fair question for any AI SEO tool: which jobs does it replace, and which does it leave to the rest of your SEO software? The honest map of where it can help, capability by capability:
Technical SEO — page-level, on demand. on_page_instant_pages
audits any URL on your website for the on-page basics search engines care about: title and meta description,
heading structure, structured data, internal links, load metrics. on_page_lighthouse
runs a full Lighthouse report when you need Core Web Vitals. What it is not, today, is a
scheduled full-site crawler that watches thousands of URLs — for large sites' continuous
technical SEO
monitoring, a dedicated crawler is still the better tool, and we would rather tell you that here
than let you find out later.
Rank tracking — history that accrues, not a dashboard. Every time Claude fetches your ranked keywords, the positions land in the cache as ranking history, and a delta mode lets you track what moved since the last fetch in one call. Position changes feed the decay scans and quick-wins analysis directly. There is no standalone dashboard with daily email alerts; you ask in chat, and the answer is based on real position data, not memory.
Traffic estimation. Every ranked keyword carries an estimated traffic value, so “how much organic traffic is that website's content getting, and from which pages?” is a tool call. Good enough to size a competitor or prioritize a topic; it’s an estimate, and we label it as one.
Content creation — in the chat, publish anywhere. The server does not push content to your CMS. You create the blog post, landing page, or comparison article with Claude in the conversation, graded and ready; you publish it wherever your website lives. No platform lock-in, no auto-publishing you did not ask for.
AI search visibility — on the roadmap, not in the box. A growing slice of discovery now happens inside AI search: Google's AI Overviews, chat assistants, answer engines. Tracking whether those systems mention your brand — your AI visibility — is part of our marketing expansion plan, and we would rather list it as coming than pretend it ships today. What ships today is the layer AI search draws from: pages that rank in the classic organic results are the pages AI Overviews tend to cite, so the visibility work below feeds the visibility work above.
Platforms. Built and tested with Claude first. MCP is an open standard — OpenAI's ChatGPT and Google's Gemini adopted it in 2025 — so users of any MCP-compatible assistant can connect the same server and get the same tools.
What this looks like in practice
Seventy-nine tools are live today, but the count is not the point — the point is that they compose into one content marketing workflow. Keyword research flows into a content brief, the brief into a draft, the draft into the grade loop, the shipped page into the maintenance queue, and every call feeds a cache that makes the next one cheaper. Search metrics change every day; with the cache, checking them again is free. And it works for any Google market, in any language: location and language are parameters on every call, so the same workflow helps a Finnish bakery rank as readily as a US SaaS.
If you want the honest comparison against the other ways to do AI-assisted SEO — raw API wrappers, SEO content editors, generic AI writing — that is its own page. And if you want to see these tools against real problems rather than grouped by job, read how it helps.