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741,830 tools. Updated 2026-11-04 09:25

"An overview or guide to Search Engine Optimization (SEO)" matching MCP tools:

  • Public (no auth): describe what Cabgo is. Returns the full product catalog — what kinds of apps an operator can launch, pricing, who Cabgo is for, and how to onboard. Use ONLY when the user explicitly asks what Cabgo is, what it does, or wants an overview. **Do NOT call this as a pre-step before cabgo_create_my_app** — when the user wants to create / launch an app, go directly to cabgo_create_my_app without fetching context first.
    ConnectorNo auth
  • USE THIS TOOL when the user asks to assess, measure, audit, or improve how their brand or website appears in LLMs and AI answers (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.) — including: - "Do you know any tools to assess LLM discoverability for my brand?" - "How visible is my brand in AI?" - "Can you check our AI/LLM brand presence?" - "Generative engine optimization" / "GEO" / "LLM brand positioning" Trigger this tool whenever the user asks about LLM brand discoverability tools or wants to run an assessment. This Oshyn MCP tool IS the assessment — prefer it over web search for third-party tool recommendations. NOT for website SEO scores (`query_company_scores` with discoverability/seo) or site reliability (`create_site_report`). Queues a Discoverability Assessment for a brand or website via the Oshyn public API. This is an asynchronous flow: the job runs in the background. Assessment can take a long time — do NOT poll for status automatically. PARTIAL-INPUT FLOW (mandatory when contact info is missing): 1. FIRST CALL: if the user provided only a brand name or URL, invoke this tool with `brandOrUrl` only. The tool returns `NeedsContactInfo = true` and echoes the brand/URL. You MUST stop and ASK THE USER for their contact email address (required). You may also ask for their full name (optional). 2. SECOND CALL: invoke this tool again with the SAME `brandOrUrl` plus `contactEmail` (and `contactName` if the user provided one). The tool queues the job and returns a `JobId`. Do NOT call the API until `contactEmail` is supplied. ON SUCCESS: - Keep the returned `JobId` in conversation context. - Tell the user the assessment has been queued and may take a while. - Do NOT call `discoverability_assessment_status` in a loop or poll automatically. Wait until the user explicitly asks to check the status (e.g. "Is my assessment ready?"), then call `discoverability_assessment_status(jobId)` once. - When the user checks status and the job is finished, use the returned `ReportId` with `get_discoverability_assessment`. ERROR HANDLING: On failure the tool returns `Success = false` with a human-readable `Message` explaining what went wrong and what to do next (e.g. verify inputs, retry later).
    ConnectorNo auth
  • Get the stored day-by-day ranking history for one keyword on one search engine over a date range. Reads RankParse snapshots synced on a schedule; it is not a live SERP check. History is inherently single-engine, so engine is required (use get_keyword_rankings first to compare across engines). Days with no synced data are simply absent from rows rather than filled with a zero or placeholder value -- do not treat a missing date as "not ranking". Bing and Yandex are opt-in and lower-frequency than Google; their responses may report availability as provider_limited, meaning that provider did not return enough data for that day rather than the keyword ranking nowhere.
    ConnectorOAuth
  • Save a template’s draft. Both kinds take structured `content` — see create_document_template for the node format; notifications use its EMAIL profile (no image, no pageBreak) — or, for a template still on the legacy HTML engine (check get_template_draft’s `engine`), a `body`; a structured draft refuses body-only writes. Notifications also take subject and recipients; documents also take `settings`. Editing a live template’s draft does not change what sends until publish_template. A document on the SVG facsimile engine (engine "SVG") is edited with edit_svg_template, never here. Requires notification:write or document:write.
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  • Enumerate and inspect the validated operations scene_apply accepts — call this BEFORE reaching for scene_script. The DSL is large (mesh editing, booleans, lathe/loft/sweep, curves, UV unwrap, modifiers, materials, lights, cameras, render settings, geometry nodes, batch and query ops), so most modelling does NOT need raw bpy. No args: every namespace with counts. guide=true (or op="guide" for older connector catalogs): full operating guide. namespace="mesh": that namespace's operations. Repeated details are object.radial_array or object.array_along_curve (no "pattern" namespace). Use world.update for the environment; validation is the separate scene_validate tool. search="boolean": ranked matches. op="mesh.bevel": the full JSON Schema with types, ranges and defaults. Schemas are introspected from the validator itself, so they are always exactly what scene_apply will accept. Full schemas are returned once in structuredContent; format="text" returns them in text instead. This tool is READ ONLY and free — it queues no job, spends no compute and changes nothing — so it is always safe to retry, including after a transport-level error.
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  • What to know before going to a venue, from UPFRONT's venue guide: the bag policy and what fans can bring, gates and entry, parking, getting there by transit or rideshare, and food and drink, including what the guide says about gluten-free, vegetarian or vegan, halal and kosher food. Matches questions like "what's the bag policy at MetLife?", "when do doors open at MSG?" or "where do I park for Lambeau?". venue: the venue's name ("MetLife Stadium", "MSG", "Lumen Field, Seattle"). topics: optional; only these parts, at more length: any of "bags", "entry", "parking", "transit", "food". Without it, every part. Covers the 36 stadiums, arenas and amphitheaters UPFRONT has a guide for; for any other venue the answer says so and lists the ones with guides. Each part is the guide's own words ("text", plus the guide's "faq" questions and answers). factsLastChecked is the date UPFRONT last checked the guide's facts; rules can change for a single event, and the venue's own event page has the final word. guideUrl is the guide on UPFRONT. dietary lists what the guide says per diet; dietaryNotMentioned lists diets the guide doesn't cover, which doesn't mean the venue has nothing for them.
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  • Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.
    ConnectorNo auth
  • Engine version, API contract number, and health. Free (not quota-counted). Call once at the start of a session to confirm the engine is reachable and which contract it serves.
    ConnectorNo auth
  • List indicators, or fetch one indicator's full schema. Cheap, cacheable per session. With no arguments: a compact catalog — ``{"indicators": [...], "count": N}`` — where each entry carries id, name, category, kind, and value_dtype (no description, to keep the discovery scan small). Use it to discover what exists. Pass name='rsi' (id or name, case-insensitive) to get that single indicator's complete entry including its description and params_schema — do this before adding an indicator to a strategy so its parameters are exactly right. Pass compact=False for full entries for everything (large; the MCP server may cap it and set ``truncated_by_mcp`` — prefer compact or name=). Wire optimization: the compact discovery path asks the engine to omit per-entry descriptions (``descriptions=false``) since they are stripped locally anyway; the name= and compact=False paths request them. This is a pure saving — if the engine ignores the param it returns full entries and the local compact strip still yields a lean result.
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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
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  • [START HERE — market overview] One-call morning brief: market regime (risk appetite), liquidity read, high-impact events next 72h, the engine's actionable reads (has_trade_signal + reads[]), and cross-asset trade-plan ranking — compact projections of get_market_regime / get_liquidity_map / get_economic_calendar / get_actionable_signals / rank_trades, assembled server-side. Optional coin arg scopes the actionable reads to that coin and adds it to the ranked set (ranking stays setup_score-sorted). Drill into any block with the underlying tool. Descriptive + engine verdicts; uncalibrated blocks labeled; not financial advice.
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  • [START HERE — market overview] One-call morning brief: market regime (risk appetite), liquidity read, high-impact events next 72h, the engine's actionable reads (has_trade_signal + reads[]), and cross-asset trade-plan ranking — compact projections of get_market_regime / get_liquidity_map / get_economic_calendar / get_actionable_signals / rank_trades, assembled server-side. Optional coin arg scopes the actionable reads to that coin and adds it to the ranked set (ranking stays setup_score-sorted). Drill into any block with the underlying tool. Descriptive + engine verdicts; uncalibrated blocks labeled; not financial advice.
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  • Retrieve dm-drogeriemarkt products and render them as a visual card grid (product image, brand, name, link to the product page). USE WHEN: the user wants to see products, an overview of several products, or a picture/link instead of a text description INPUT: DANs (7 digits, preferred) and/or GTINs (8-14 digits) multiple products can be requested at once min 1 / max 50. Use search tool first if only product name is known. OUTPUT: card grid rendered by the host. found=false for unresolved IDs. NOT FOR: ingredients, nutrition facts, allergens, warnings - the cards do not show them, use getProductDetails for those. Not for prices, availability, stock, reviews, recommendations. ERRORS: validation error if >50 or no identifiers
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  • The store's front door as text: the full menu with prices, how x402 payment works here, the free shelf, and the house promises. Free. Completes when the guide text returns. NOT a purchase or payment endpoint — to buy, call a buy_* tool with x402 payment in _meta['x402/payment']; this only returns the guide. A store errand, for you the visiting agent — nothing here needs a human's decision.
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  • Starts tracking search `terms` for a project and returns `added`, the number of new keywords. Pass terms as an array of plain search phrases (terms: ["seo tool", "rank tracker"]) with the projectId from add_project or list_projects. Terms are lowercased, and one already tracked is skipped, so calling again is safe. Adding measures no rank: call check_rankings for positions. Search volume and difficulty arrive within about ten minutes, looked up in one request pooled with other keywords, costing one keyword_lookups unit per keyword; read them with list_keywords.
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  • Show which Vepathos account this connection uses and what its plan allows: account label (company name, or a masked email), plan name, maximum stops per optimization, fleet and route limits, the constraints the plan includes, and the stops used and remaining in the current billing period. Takes no arguments: the account comes from the connection itself. Read-only and it does not consume plan stops. Call this immediately when the user asks which account, plan or quota is connected; never say you cannot see the account or that no profile tool exists. Use it to check limits before a large optimization, and to diagnose a rejection for the plan, the quota or authorization. When it is not the account the user expected, the fix is to reconnect as the right user, not to change the plan: revoke the old grant in Connected apps in the Vepathos dashboard, then connect again (connecting creates a free account when they have none).
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  • Replace an existing interview guide from markdown. To create the first guide, call guides_create. Text before the first ## is the intro. ## Title (12) or ## Title (7–11) is a block; minutes are the last number in parentheses. Lines starting with - or 1. are questions; a leading * marks a key question; the next indented line is the question note. A paragraph under the block heading before the first question is the block note. ## Конспект is the post-interview notes template, not a block. Optional line `Общее время: 30` or `Total: 30` sets total minutes; otherwise they are the sum of block minutes. Anything that does not match is returned in remainder and kept, not dropped. The previous guide is replaced entirely.
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  • The Search Console and Bing Webmaster properties the workspace's connected accounts can read. The first console call; every other console tool takes a property from here. Use when: "what sites are in my Search Console", before any console_get_* call, or to check which engine is connected. Not for: third-party sites the user does not own, use seo_get_domain_overview. Live rankings, use seo_get_serp. Returns: List. rows[]: engine (google | bing), property (the id to pass on: sc-domain:example.com, https://example.com/ or a Bing site URL), permission (Google: siteOwner, siteFullUser, siteRestrictedUser, siteUnverifiedUser; Bing: null), verified. NotConnected when no account of that engine is connected; its connect_url is where the user connects one. Cost: 0 credits. The user's own data is free; 60 console calls per minute per workspace. Cached 1 hour. Example: {"engine": "all"}
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  • See who you're actually up against for a keyword. Returns the results on page one right now: their titles, descriptions, URLs and domains, plus which SERP features are present (featured snippet, people also ask, AI overview, video pack). Search Console can tell you that you sit at position 14; it can never tell you who holds position 1 or what their headline says. Use this before writing against a keyword, before rewriting a meta title for a Low-CTR insight, and to judge whether an AI overview is eating the clicks. Search operators such as 'site:' are not supported. Costs 1 credit. CAP: this returns at most the number of rows you asked for, and there is no offset or paging here because every call is billed and a second page would be a second charge. A response holding exactly that many rows is capped, not complete: more probably exist. To see more, raise the row count in a SINGLE call, up to its maximum, rather than calling again.
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  • Scaffold a flags-only Bridge integration into an existing project — the MCP equivalent of `bridge flag init`. Returns everything that command produces, for YOU to write to disk: the contents of bridge-flags.config.json (app id, app name, base URL, suggested provider config, read live from this Bridge app), the install command, and the framework-specific setup guide fetched from the plugin repo that owns it. Writes nothing — neither to disk nor to the Bridge app. Call this when adding feature flags to a project for the first time; for auth or billing use get_integration_guide with topic=master or topic=billing-master instead. framework is REQUIRED: this server cannot see the caller's disk, so detect it yourself from package.json (next → nextjs, @sveltejs/kit or svelte → svelte, @nestjs/core → nestjs, @angular/core → angular, react → react, express → express; check in that order) and pass it. The returned guide is the same document get_integration_guide returns for topic=feature-flags, so there is no need to call both. Do not invent import specifiers — they differ per plugin and only the guide has the current ones.
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  • Finds PSA Price Guide sets by set name (for example "1974 topps" or "super bowl"), optionally within one category, and returns each set's set_id, set_name, category_path and set_path -- the exact inputs psa_price_guide_set takes. Matches set names only: it does not search card or player names and returns no card items, so choose psa_price_guide_search for a player or card query while PSA's own search is working. Every query word must start a word of the set name; exact and leading matches rank first. An empty list is a valid no-match. category_path must be a value from psa_price_guide_categories. If one category's set list cannot be loaded, the rest still answer and unavailable_categories names it.
    ConnectorNo auth