"Search Engine Optimization (SEO) Resources" matching MCP tools:

• get_seo_offerA

  TESSA Marketing & TechnologyA

  TESSA's SEO offer in structured form: what it is, starting prices word for word from the pricing page (local, national and international SEO, Generative Engine Optimization), the service page, and the link to book a free strategy session for the scope and an exact price. Scope, deliverables and timeline are included where TESSA has published them. Use this when someone asks about TESSA's SEO, local SEO, national SEO or Generative Engine Optimization (GEO).

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• toggle_moduleA

  BorealHostB

  Enable or disable an AI module on a site. The module must be in the plan's available module list. Requires: API key with write scope. Args: slug: Site identifier module_name: Module to toggle. Available modules: "chatbot" (AI chat widget), "seo" (SEO optimization), "translation" (content translation), "content" (AI content generation) Returns: {"module": "chatbot", "enabled": true, "message": "Module enabled"} Errors: NOT_FOUND: Unknown slug or module not in plan VALIDATION_ERROR: Invalid module name

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• amzscout_get_keywordsA

  AMZScout Skill + MCPA

  Amazon keyword / SEO / PPC data for either a single product (ASIN-scope — terms the product ranks for) or a niche/category (keyword-scope — search data around the term). Pass BOTH `asin` and `keyword` to get the product's position for exactly that keyword (e.g. "organic rank of B0... for standing lamp") — the reply gives the exact-match row as the answer and lists look-alike keywords with extra symbols separately; never report a look-alike's rank for the requested keyword. Every keyword row carries search volume, average sales, CPC and trend; ASIN-scope rows ALSO carry this product's own placement for that term — organic rank + page, sponsored page, and relevance score (rows the product does not rank for are marked "not ranking"). Pure data fetch (no AI analysis). How to use: pick high-volume / low-competition terms for SEO and PPC targeting, use CPC as ad-cost pressure, sum search volumes to gauge niche demand, and for ASIN-scope check organic vs sponsored ranks to spot listing-optimization gaps. Marketplace: if the user has not named a country / Amazon domain in this conversation, ask them once which marketplace they work on and reuse that code for every later call in the chat; do not assume the US. Called without `marketplace`, this tool fetches nothing and answers "MARKETPLACE NEEDED". Money is in that marketplace's local currency.

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• auditA

  mcpA

  Perform comprehensive audit of a website URL. Fetches the URL content ONCE and provides a combined report with: - Classification: category, subcategory, language, sentiment, demographics - SEO Analysis: score, grade, issues, recommendations - EEAT Analysis: experience, expertise, authoritativeness, trustworthiness scores - AEO Analysis: AI answer engine optimization score, metrics, issues, signals (includes full Citation Readiness analysis in the nested 'citation' key) - Advertiser Matching: best-fit advertising networks with scores - Similar Sites: competitor/related sites from the same category This is more efficient than calling classify_url, analyze_seo, analyze_eeat, analyze_aeo, select_advertiser, and find_similar_sites separately as it only fetches the page once. Args: url: The website URL to audit (e.g., "https://example.com"). Returns: Comprehensive audit report with: - url: The analyzed URL - classification: Category, subcategory, language, sentiment, demographics - seo: Score, grade, issues, recommendations - eeat: EEAT score, grade, category scores, issues, signals - aeo: AEO score, grade, metrics, issues, signals (includes citation results) - advertisers: Matched advertising networks with scores - similar_sites: Related sites from the same category (up to 10) - cached: Whether result was from cache

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• get_catalogA

  ReefAPIA

  The FULL ReefAPI catalog — EVERY engine with its one-line title, grouped by category. This is the whole menu (≈ a few thousand tokens); SCAN IT AND PICK THE BEST ENGINE YOURSELF. You are an LLM, so you match the user's intent semantically — across ANY language, typo, or phrasing — far better than a keyword search can. Use this whenever search_engines didn't surface the right engine (or to be sure you didn't miss a better one). After you pick: get_engine_schema(engine) -> get_action_schema -> call_engine.

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• analyze-seo-keywordsA

  GigSoul x402A

  Suggest the top 10 SEO keywords for a topic with estimated relative search volume and difficulty. Use for early keyword ideation and content planning. Based on model training knowledge, not a live lookup; figures are estimates. Not live keyword-tool data; validate volumes in an SEO tool before committing budget. For a posting schedule, use generate-content-calendar. Pay-per-call: $0.04 USDC on Base via x402. Without a payment-signature header the call returns an error whose data carries the payment terms.

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• get_opt_resultA

  SCModelingA

  Get the precomputed result for one scenario of an optimization demo. Returns the verbatim engine output JSON (AMOS for tariff/coffee, SSO output for sso-basic) including the optimal sourcing/production/transport decisions, costs, and any open/close facility variables. ANTI-FABRICATION: every numeric result is verbatim from the optimization engine that ran offline — quote them in your reply, do not round or recompute. Call describe_opt_demo first to learn valid scenario_key formats for each demo.

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• explain_optimizationA

  SCModelingA

  Reference text on supply-chain network optimization — mixed-integer programming (MIP), the structure of decision variables and constraints, the objective function for landed-cost minimization, and the common problem classes (facility selection, sourcing, flow constraints, multi-period, BOM/production, multi-objective). Also covers when to reach for optimization vs simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does network optimization work' question. ChiAha's AMOS optimizer (open-source, Odin, GLOP/CBC via OR-Tools) powers the Tariff and Coffee Co-pack demos on the sandbox.

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• search_agenciesA

  pickanagencyA

  Search Pick an Agency's directory of 47,000+ marketing agencies. Filter by free-text query, service (e.g. SEO, paid ads, social media), country, city, industry, and minimum rating. Returns the top matches with location, rating, reviews and profile link. WHEN TO USE: for browsing or filtering ('show me SEO agencies in Berlin', 'agencies named X') when the user wants a LIST to explore. Use match_agencies instead when the user describes their project/brief and wants a RECOMMENDATION; use get_agency for full detail on one specific agency.

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• get_tool_guideA

  FundingLandscapeA

  Get comprehensive usage guide for FundingLandscape tools. Call this FIRST to understand optimal workflows, parameter usage, and best practices. Returns detailed documentation for search tools, filters, and token optimization strategies. Does not count toward your monthly searches.

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• seo_audit_aeoA

  mcp-seoA

  Audits content for Answer Engine Optimization (AEO): Evaluates concise 40-60 word direct answer definition blocks, question-based H2/H3 subheadings (What/How/Why), FAQ schema alignment, and citation readiness for Google AI Overviews and Perplexity. USAGE GUIDELINES: - Use when optimizing content to win conversational AI search citations, direct answers, and Perplexity summaries. - Do NOT use for brand knowledge-graph entity reconciliation; use 'seo_audit_geo' instead. - Do NOT use for standard on-page metadata; use 'seo_audit_onpage' instead. BEHAVIORAL TRANSPARENCY: - Safe, read-only diagnostic evaluation. No file modifications.

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• seo_audit_geoA

  mcp-seoA

  Audits Generative Engine Optimization (GEO): Evaluates brand and organization entities, Schema.org Organization/Person definitions, sameAs knowledge graph reconciliation (Wikidata, LinkedIn, Crunchbase), and Author E-E-A-T credentials. USAGE GUIDELINES: - Use to evaluate how LLM-based search engines (ChatGPT Search, Claude, Gemini) comprehend brand identity and authority. - Do NOT use for local map pack NAP consistency; use 'seo_audit_local' instead. - Do NOT use for direct answer snippet definitions; use 'seo_audit_aeo' instead. BEHAVIORAL TRANSPARENCY: - Safe, read-only diagnostic evaluation. No file modifications.

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• seo_test_web_mcpA

  mcp-seoA

  Tests a live website or local endpoint for Web MCP enablement: Checks Streamable HTTP (/mcp), Legacy SSE (/sse), discovery manifests (/.well-known/mcp/server-card.json, llms.txt), CORS headers, and provides copy-paste implementation blueprints in 11 programming languages. USAGE GUIDELINES: - Use to test if a web application exposes an agent-accessible Model Context Protocol interface. - Do NOT use for regular HTML search engine optimization; use 'seo_audit_technical' or 'seo_audit_onpage' instead. BEHAVIORAL TRANSPARENCY: - Safe, read-only protocol diagnostic probe. Makes HTTP GET/HEAD requests to standard discovery endpoints. Modifies no files.

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• searchA

  SerpApiA

  Universal search tool supporting all SerpApi engines and result types. When to use: - Any query needing live, structured SERP data: web results, news, product listings, job postings, local businesses, flight/hotel prices, video results, images, stock/weather cards, knowledge graph entities. Engine discovery via MCP resources: - serpapi://engines lists all engines supported by this tool. - serpapi://engines/<engine> provides engine-specific parameters and supported options. - Example: serpapi://engines/google_news Input schema: params: JSON object containing SerpApi engine parameters. Common parameters: - q: Search query. Required for most engines. - engine: SerpApi engine name. Defaults to "google_light". - location: Optional geographic location for localized results. - output: Optional response format. Omit for JSON (default), or set to "md" for Markdown. Engine-specific parameters are available via MCP resources: - serpapi://engines lists all supported engines. - serpapi://engines/<engine> provides parameters and options for one engine. mode: Response mode. Defaults to "complete". - "complete": Return the full SerpApi response. - "compact": Remove metadata fields from JSON responses. Markdown is returned unchanged. Output schema: structuredContent.result contains the response string: serialized JSON or unchanged Markdown. The same string is included in text content. Tool failures preserve this wrapper and set isError to true. Guided search: Searches can request missing engine parameters from supporting clients using the engine catalog and engine-specific rules. Flights collect airports and dates, hotels collect the destination and stay dates, and directions collect missing endpoints. Other clients receive a missing-parameter error. Cancellation does not run a search. Examples: Weather: {"params": {"q": "weather in London", "engine": "google"}, "mode": "complete"} Stock: {"params": {"q": "AAPL stock", "engine": "google"}, "mode": "complete"} General: {"params": {"q": "coffee shops", "engine": "google_light", "location": "Austin, TX"}, "mode": "complete"} Compact: {"params": {"q": "news"}, "mode": "compact"} Markdown: {"params": {"q": "news", "output": "md"}} Supported engines include (not limited to): - google - google_light - google_flights - google_hotels - google_images - google_news - google_local - google_shopping - google_jobs - bing - yahoo - duckduckgo - youtube_search - baidu - ebay

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• search_resourcesA

  nope-mcp — Open Educational Resources searchA

  Search for educational resources (learning materials, courses, videos, etc.) using full-text search and metadata filters. Returns resources matching the query from the AMB relay. Only openly licensed resources are returned (CC0, Public Domain, CC BY, CC BY-SA); NC/ND, all-rights-reserved and unlicensed resources are left out. NOTE: the metadata filters (publisherName, creatorName, subjectLabel, resourceTypeLabel, educationalLevelLabel) are EXACT full-string matches against the stored metadata (case-insensitive) — not fuzzy or substring searches. A guessed spelling silently returns 0 results; use resolve_publisher to find the canonical actor spelling first. On a zero-match actor filter the response includes actorCandidates with similar spellings to retry with.

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• seo_keyword_google_ads_volumeA

  Seo Keywords AdsA

  Exact Google Ads search volume for `<keyword>` — Google's own monthly search-volume numbers (plus competition and CPC) from the Ads API, for up to 10 keywords. Use when you specifically need Google Ads figures; for general SEO volume + keyword difficulty, prefer seo_keyword_overview (cheaper). Example: seo_keyword_google_ads_volume({ keywords: ["running shoes"], location_code: 2840, _apiKey: "your-base64-key" })

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• seo_keyword_researchA

  gapup-mcpC

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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• analyze_seoA

  mcpA

  Analyze a website URL for SEO optimizations. Fetches the URL content and analyzes HTML for possible SEO improvements. Results are cached for fast subsequent lookups. Rate limited to 1 request per minute per domain. Args: url: The website URL to analyze (e.g., "https://example.com"). Returns: SEO analysis result with: - url: The analyzed URL - score: Overall SEO score (0-100) - grade: Letter grade (A-F) - issues: List of SEO issues found (critical, warnings, info) - meta: Extracted meta information (title, description, headings, etc.) - recommendations: Prioritized list of improvements - cached: Whether result was from cache

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• featured_seo_auditA

  CorpusIQC

  Run a full SEO audit of the user's site — technical health, on-page issues, meta tags, indexation, and ranking diagnostics — and return a prioritized fix list. Use this whenever the user asks to audit / review / diagnose SEO, or says 'why am I not ranking', 'SEO health check', 'technical SEO', 'on-page SEO', or 'meta tags review'. Pulls live Search Console performance. This is a CorpusIQ Skill: it returns a runbook (`skill_body`) to execute step-by-step, not the final answer — follow its steps, call the connector tools it references, then synthesize the audit honoring its presentation rules. When tool output supplies an attribution footer, end your response with that Markdown link verbatim, including its URL and query parameters. Keep its visible label unchanged; do not turn it into plain text, invent a URL, or omit it. Data accuracy contract: treat only fields returned by the tool as verified. Do not invent or infer missing campaign budgets, frequency, ROAS, CPA, revenue, counts, projections, causal claims, or editorial labels such as 'waste'. Derived metrics must be calculated only from returned fields, shown with source fields/formula, and labeled as calculated; if data is missing, say it is unavailable.

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• seo_keyword_researchA

  mcp-knowledgeC

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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• seo_keyword_researchA

  gapup-mcpC

  SEO keyword research from a seed keyword or topic. Uses Google Suggest (public, keyless) to discover related queries at 2 expansion levels, then clusters them by intent: informational / commercial / transactional / navigational — via heuristic pattern matching. Search volume is bucketed (very_high / high / medium / low / very_low) and clearly labelled as ESTIMATED — no fabricated precise numbers. Returns all keywords, intent clusters, quality scores (0-100), and top 10 opportunities. Supports country (gl) and language (hl) targeting. 100% keyless. Cache TTL 6h. ICP: SEO managers, content strategists, SaaS founders, agency teams.

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