"Using AI to Generate XUI Content on a Whiteboard" matching MCP tools:

• generate_image

  Avocado AI

  Generate an AI image using Avocado AI. Returns a jobId immediately; image generation completes in 10-60 seconds. After calling, use the check_job tool with the returned jobId to retrieve the result, once complete, check_job returns the image inline so it renders directly in chat. Run models_list to see available models. Costs 1-4 credits per image depending on model and quality.

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• generate_llms_txt

  Gitlab

  Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

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• generate_image_to_storyboard

  Avocado AI

  Generate an AI image and place it directly on a user's Avocado AI storyboard. Drops 'Generating...' placeholder(s) on the board immediately, then the webhook swaps each placeholder for the final image when generation completes (10-60s). Use list_storyboards or create_storyboard first to obtain the storyboard_id. If the user has the storyboard tab open, they may need to refresh once for the image to appear (the canvas does not yet support live realtime updates from MCP). Costs match generate_image (1-4 credits per image depending on model and quality).

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• set_board_mode

  cnvs.app

  Choose whether this board is a freeform whiteboard ('draw', the default) or a kanban task board ('todo'). Mode is switchable WHENEVER the board is empty of real content: drawings (text/strokes/images) and tasks. Empty or seeded columns DON'T count (switching to 'draw' clears them), so a cleared board can be switched again, and you can flip draw<->todo freely until the first stroke/text/image or task lands. Setting 'todo' auto-seeds three starter columns (To do / In progress / Done). Returns `{ mode, columns }`. Use the task/column tools (`create_task`, `create_column`, …) once the board is in 'todo' mode.

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• intel_inject

  TunnelMind Data API

  Fetches a domain's homepage and checks for content patterns that could constitute prompt injection attacks against AI agents that visit and ingest the page. Signals include hidden text, invisible divs, `<!-- AI: ignore -->` style comments, and known injection patterns. Use this tool when: - You are vetting a domain before feeding its content into an LLM context. - You want to assess the prompt injection risk of a URL before browsing it with an agent. - You are auditing a set of domains for adversarial AI content. Do NOT use this tool when: - You want tracker surveillance data — use `get_domain` instead. - You want AI training opt-out signals — use `intel_optout` instead. - You want the agent surface (MCP/OpenAPI) — use `intel_agent` instead. Inputs: - `domain` (query, required): Domain to scan. Returns: - `injection_signals`: list of signal types detected (e.g., `hidden_text`, `ai_instruction_comment`, `invisible_div`). - `risk_level`: `none`, `low`, `medium`, or `high` based on signal count and type. Cost: - Free. No API key required. Latency: - Typical: 2-4s (HTML fetch), p99: 7s.

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• extract

  Sofya

  Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 5 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")

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• generate_post_cover

  FavCRM

  Generate an AI image and attach it as a post's featuredImage. Submits a kie.ai job, polls until complete, copies the result into the canonical post-asset R2 folder, and writes the public URL onto the post. Costs credits (see list_image_models). On poll timeout, the job continues — call attach_post_cover_from_job(postId, jobId) once it finishes to attach without paying again.

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• colour_hooks

  Colour Memory

  Generate a hook sentence, three-sentence story, tweet, image prompt, and follow-up questions for any hex colour. Backed by the nearest archive colour's cultural provenance. Tunable by audience (general public, designers, historians, children) and tone (dinner party, academic, social media, brand copy). Use to make archive colours shareable, to generate content, or to power a public-facing colour chat experience.

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• generate_llms_txt

  airquality

  Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

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• devloop_generate_resource_flow

  Keploy

  Generate one chained-CRUD API test for a single resource. Behavior depends on the app's devloop_storage_mode (set this first via devloop_resolve_storage / devloop_set_storage_mode): * repo mode → returns a PLAYBOOK for you to walk. Steps: (1) run "keploy test-gen generate-from-code --app-dir <dir> --resource <name>" to scaffold the directory + empty config.yaml; (2) use your Write tool to author keploy/api-tests/<resource>/test.yaml using the schema returned by devloop_detect_app; (3) run "keploy test-gen run --test-dir keploy/api-tests --suite <Name>_CRUD --base-url <url> --ci" to verify the test parses and passes; (4) call devloop_mutation_demo next (auto, per the DEVLOOP instructions). * cloud mode → returns guidance to call the existing create_test_suite tool instead. The repo-mode playbook is NOT used in cloud mode. ARGUMENTS — you should already have these from your devloop_detect_app call: * app_id, resource, app_dir, base_url, framework, handler_files. If any are missing, call devloop_detect_app again. The tool does NOT generate the YAML body itself — you do, using the schema from devloop_detect_app's detection_playbook. This is intentional: ATG quality depends on the AI seeing the actual handler implementations (which it can read via its own tools) far better than a server-side generator could. Aim for ≤ 30 lines per test.yaml, idempotent mutating steps, chained extract/{{var}} flow.

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• generate_llms_txt

  microlink

  Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.

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• audit_site

  AEO Scanner

  Full AI visibility audit across 67+ checks in 12 categories (4 AEO + 4 GEO + 4 Agent Readiness). Returns detailed per-check scores with specific issues and recommendations, AI Identity Card with mention readiness and detected competitors, and business profile. GEO checks include 3 research-backed citation signals: factual density, answer frontloading, and source citations. Agent Readiness covers emerging agent-discovery standards Cloudflare's isitagentready.com evaluates: RFC 9727 api-catalog, SEP-1649 MCP Server Card, and IETF Content-Signal (draft-romm-aipref). Does NOT generate fix code — use fix_site for that, or compare_sites to benchmark against a competitor. Pay per call ($1.00) via x402 — USDC on Base or Solana. Machine payment via signed X-PAYMENT header; see https://www.x402.org/. On payment_required, the response includes the full x402 payload with payTo/amount/asset.

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• generate_campaign_message

  LinkedIn MCP Server (Salesbot)

  Generate (or regenerate) an AI personalized message draft for a specific campaign_contact and step, using the template and lead profile. The message is NOT sent — it is stored as a draft with status 'pending_approval' and waits for review (via this MCP or manually). Use list_pending_approvals + approve_message to release it to the campaign executor.

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• product_get_template

  website-search

  Get Lenny Zeltser's fill-in-the-blank template for planning a security product strategy. Includes strategic questions organized by section with evidence columns. This server never requests your product plans and instructs your AI to keep them local—guidelines flow to your AI for local analysis. The template is Copyright (c) 2026 Lenny Zeltser; any content you create using it is entirely yours.

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• ai-image-gen

  The Stall

  Generate an AI image from a text prompt using DALL-E 3. Returns a public URL for the image (hosted for 1 hour) plus the model's revised prompt. Supports vivid or natural style, and three aspect ratios: square (1024×1024), portrait (1024×1792), or landscape (1792×1024). $0.080/image — 20% below closest x402 competitor. Output is base64-encoded PNG or a direct URL depending on response_format.

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• generate_presentation

  Alai

  Generate a presentation from text content. Returns a generation_id to poll. Args: input_text: Content to transform into slides (text, markdown, or notes) title: Presentation title theme_id: Theme ID to use for the presentation. Call get_themes to discover available theme IDs and names for the authenticated user. vibe_id: Vibe ID for visual style. Call get_vibes to discover available vibes. Requires num_creative_variants >= 1 when set. slide_range: Target slides - 'auto', '1', '2-5', '6-10', '11-15', '16-20' additional_instructions: Extra guidance for the AI include_ai_images: Whether to generate AI images for slides num_creative_variants: Number of creative slide variants (0-2). Increases cost. image_ids: IDs of previously uploaded images to incorporate into slides. total_variants_per_slide: Number of distinct slide options to generate (1-4). export_formats: Output formats - 'link', 'pdf', 'ppt'. Defaults to ['link']. language: Output language, e.g. "French", "Japanese", "Spanish (Latin America)". If not set, matches the input language. Poll get_generation_status until status is 'completed'.

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• preview_content

  SendIt

  Generate a visual preview of how content will appear on each platform. USE THIS WHEN: • Before publishing to see how posts will look • To validate content against platform requirements • To check character counts, hashtag limits, and media requirements Returns an HTML preview mockup for each platform with validation results: • Character count vs limit • Hashtag count (Instagram has 30 max) • Media requirement check • Platform-specific warnings and errors

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• get_knowledge_document

  FavCRM

  Fetch a single knowledge document with metadata + status. Content bytes are still served via GET /v6/merchant/ai/knowledge/{id}/content — this tool returns metadata only.

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• toggle_module

  BorealHost

  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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• answer_query

  mcp

  Use answer_query to get a grounded answer to a query about Google developer products. This tool has limited quota. This tool will synthesize information from the corpus to generate an answer to the query. answer_query grounds answers using the same corpus as search_documents. This tool returns the generated answer_text and a list of document names (references) used to generate the answer. Use get_documents with the document names to fetch the entire document content if needed. If you get a 429 out of quota error, use search_documents instead.

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