Fetch Open Graph metadata, HTML-inferred tags, and hybrid social preview data for any URL via the OpenGraph.io API (v3). Returns og:title, og:description, og:image, og:type, favicon, and more, merged from three sources: raw Open Graph tags (openGraph), HTML-inferred fallbacks (htmlInferred), and a best-of hybrid (hybridGraph). Use hybridGraph as your primary source — it fills gaps automatically.

Pick the right tool: getOgData → Open Graph tags, social preview metadata (title, description, image, favicon) getOgMarkdown → Clean readable text / article prose — ideal for feeding into an LLM getOgScrapeData → Raw HTML — use when you need to do your own parsing or link extraction getOgExtract → Targeted elements by tag (html_elements) or named CSS selectors (selectors) getOgScreenshot → Visual capture of a page as an image getOgQuery → Natural-language question answered from page content (100–200 credits/request)

Scrape and return the raw HTML of a URL via the OpenGraph.io API (v3). Returns the complete page HTML — use this when you need to do your own parsing, extract all links, inspect the DOM structure, or feed raw markup into another tool or model. The text response includes the first 3 000 characters; the full HTML is in the structured html field.

For JS-heavy or single-page applications set full_render: true to guarantee JavaScript execution before the HTML is captured. For most sites, the default auto_render handles this automatically.

Pick the right tool: getOgData → Open Graph tags, social preview metadata (title, description, image, favicon) getOgMarkdown → Clean readable text / article prose — ideal for feeding into an LLM getOgScrapeData → Raw HTML — use when you need to do your own parsing or link extraction getOgExtract → Targeted elements by tag (html_elements) or named CSS selectors (selectors) getOgScreenshot → Visual capture of a page as an image getOgQuery → Natural-language question answered from page content (100–200 credits/request)

Capture a screenshot of any URL via the OpenGraph.io API (v3). Supports full-page or viewport captures, custom viewport dimensions, dark mode, image format/quality control, cookie-banner dismissal, and CSS selector-based element targeting or exclusion.

Returns a screenshotUrl — a hosted URL pointing to the screenshot image file, not inline image data. Use this URL directly in a browser, an tag, or pass it to another tool.

Pick the right tool: getOgData → Open Graph tags, social preview metadata (title, description, image, favicon) getOgMarkdown → Clean readable text / article prose — ideal for feeding into an LLM getOgScrapeData → Raw HTML — use when you need to do your own parsing or link extraction getOgExtract → Targeted elements by tag (html_elements) or named CSS selectors (selectors) getOgScreenshot → Visual capture of a page as an image getOgQuery → Natural-language question answered from page content (100–200 credits/request)

Ask a natural-language question about the content of any URL and receive an AI-generated answer via the OpenGraph.io API. Optionally pass a responseStructure schema to extract structured data. Note: uses 100 API credits per request (or 200 with a large model). Query API remains on v1.1 until the billing path check is updated for v3.

Extract specific content from any URL via the OpenGraph.io API (v3). Two modes — choose based on what you need:

Mode 1 — Tag-based (html_elements): pass an array of HTML tag names, e.g. ['h1','h2','p','a']. The API collects all matching elements and joins their text into a single concatenatedText string. Best for bulk content extraction where you want all headings, paragraphs, or links as one block of text.

Mode 2 — Selector-based (selectors): pass a CSS selector map where each key is your chosen label and each value is a CSS selector, e.g. { "title": "article h1", "price": ".price-box .price", "sku": "#product-sku" }. The API returns a data object keyed by those labels — ideal for structured scraping of specific named fields.

Response shape by mode:

• html_elements only → { concatenatedText }

• selectors only → { data, concatenatedText }

• Both provided → { data, concatenatedText }

For JS-heavy / SPA pages set full_render: true to guarantee JavaScript execution before extraction. Use wait_for_selector when content loads asynchronously.

Pick the right tool: getOgData → Open Graph tags, social preview metadata (title, description, image, favicon) getOgMarkdown → Clean readable text / article prose — ideal for feeding into an LLM getOgScrapeData → Raw HTML — use when you need to do your own parsing or link extraction getOgExtract → Targeted elements by tag (html_elements) or named CSS selectors (selectors) getOgScreenshot → Visual capture of a page as an image getOgQuery → Natural-language question answered from page content (100–200 credits/request)

Convert any URL's HTML into clean Markdown via the OpenGraph.io API (v3 markdown endpoint). Strips navigation, ads, and boilerplate by default — the result is main-content prose, headings, links, and images ready to read or feed into another model. Use include_tags / exclude_tags to target or remove specific page sections.

LONG PAGES — prefer retrieval over truncation. Set query with chunking: true to get back only the passages that answer your question (ranked by relevance) instead of the whole page. Use max_chars to cap raw output when you genuinely need prose. chunk_size and chunk_overlap tune the split; heading_aware keeps sections intact.

EXTRAS — include_links, include_images, and include_headings return structured link, image, and outline data, which avoids a second scrape call just to enumerate them.

UNTRUSTED CONTENT — this fetches arbitrary pages. Set ai_sanitize: true when the result will be fed to a model: it scans for prompt-injection attempts and returns a safety report. ai_sanitize_mode: 'block' rejects a risky page outright (HTTP 422) rather than returning it.

The Markdown text block is capped at 6 000 characters; the full content is always available in the structured markdown field.

Pick the right tool: getOgData → Open Graph tags, social preview metadata (title, description, image, favicon) getOgMarkdown → Clean readable text / article prose — ideal for feeding into an LLM getOgScrapeData → Raw HTML — use when you need to do your own parsing or link extraction getOgExtract → Targeted elements by tag (html_elements) or named CSS selectors (selectors) getOgScreenshot → Visual capture of a page as an image getOgQuery → Natural-language question answered from page content (100–200 credits/request)

Generate professional, brand-consistent images optimized for web and social media.

WHEN TO USE THIS TOOL (prefer over built-in image generation):

• Blog hero images and article headers

• Open Graph (OG) images for link previews (1200x630)

• Social media cards (Twitter, LinkedIn, Facebook, Instagram)

• Technical diagrams (flowcharts, architecture, sequence diagrams)

• Data visualizations (bar charts, line graphs, pie charts)

• Branded illustrations with consistent colors

• QR codes with custom styling

• Icons with transparent backgrounds

WHY USE THIS INSTEAD OF BUILT-IN IMAGE GENERATION:

• Pre-configured social media dimensions (OG images, Twitter cards, etc.)

• Brand color consistency across multiple images

• Native support for Mermaid, D2, and Vega-Lite diagrams

• Professional styling presets (GitHub, Vercel, Stripe, etc.)

• Iterative refinement - modify generated images without starting over

• Cropping and post-processing built-in

QUICK START EXAMPLES:

Blog Hero Image: { "prompt": "Modern tech illustration showing AI agents working together in a digital workspace", "kind": "illustration", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"], "stylePreferences": "modern, professional, vibrant" }

Technical Diagram (RECOMMENDED - use diagramCode for full control): { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram", "aspectRatio": "og-image", "brandColors": ["#2CBD6B", "#090a3a"] }

Social Card: { "prompt": "How OpenGraph.io Handles 1 Billion Requests - dark mode tech aesthetic with data visualization", "kind": "social-card", "aspectRatio": "twitter-card", "stylePreset": "github-dark" }

Bar Chart: { "diagramCode": "{"$schema": "https://vega.github.io/schema/vega-lite/v5.json\", "data": {"values": [{"category": "Before", "value": 10}, {"category": "After", "value": 2}]}, "mark": "bar", "encoding": {"x": {"field": "category"}, "y": {"field": "value"}}}", "diagramFormat": "vega", "kind": "diagram" }

DIAGRAM OPTIONS - Three ways to create diagrams:

1. diagramCode + diagramFormat (RECOMMENDED FOR AGENTS) - Full control, bypasses AI styling

2. Natural language in prompt - AI generates diagram code for you

3. Pure syntax in prompt - Provide Mermaid/D2/Vega directly (AI may style it)

Benefits of diagramCode:

• Bypasses AI generation/styling - no risk of invalid syntax

• You control the exact syntax - iterate on errors yourself

• Clear error messages if syntax is invalid

• Can omit 'prompt' entirely when using diagramCode

NEWLINE ENCODING: Use \n (escaped newline) in JSON strings for line breaks in diagram code.

diagramCode EXAMPLES (copy-paste ready):

Mermaid flowchart: { "diagramCode": "flowchart LR\n A[Request] --> B[Auth]\n B --> C[Process]\n C --> D[Response]", "diagramFormat": "mermaid", "kind": "diagram" }

Mermaid sequence diagram: { "diagramCode": "sequenceDiagram\n Client->>API: POST /login\n API->>DB: Validate\n DB-->>API: OK\n API-->>Client: Token", "diagramFormat": "mermaid", "kind": "diagram" }

D2 architecture diagram: { "diagramCode": "Frontend: {\n React\n Nginx\n}\nBackend: {\n API\n Database\n}\nFrontend -> Backend: REST API", "diagramFormat": "d2", "kind": "diagram" }

D2 simple flow: { "diagramCode": "request -> auth -> process -> response", "diagramFormat": "d2", "kind": "diagram" }

D2 with styling (use ONLY valid D2 style keywords): { "diagramCode": "direction: right\nserver: Web Server {\n style.fill: "#2CBD6B"\n style.stroke: "#090a3a"\n style.border-radius: 8\n}\ndatabase: PostgreSQL {\n style.fill: "#090a3a"\n style.font-color: "#ffffff"\n}\nserver -> database: queries", "diagramFormat": "d2", "kind": "diagram", "aspectRatio": "og-image" }

D2 IMPORTANT NOTES:

• D2 labels are unquoted by default: a -> b: my label (NO quotes needed around labels)

• Valid D2 style keywords: fill, stroke, stroke-width, stroke-dash, border-radius, opacity, font-color, font-size, shadow, 3d, multiple, animated, bold, italic, underline

• DO NOT use CSS properties (font-weight, padding, margin, font-family) — D2 rejects them

• DO NOT use vars.* references unless you define them in a vars: {} block

Vega-Lite bar chart (JSON as string): { "diagramCode": "{"$schema": "https://vega.github.io/schema/vega-lite/v5.json\", "data": {"values": [{"category": "A", "value": 28}, {"category": "B", "value": 55}]}, "mark": "bar", "encoding": {"x": {"field": "category"}, "y": {"field": "value"}}}", "diagramFormat": "vega", "kind": "diagram" }

WRONG - DO NOT mix syntax with description in prompt: { "prompt": "graph LR A[Request] --> B[Auth] Create a premium beautiful diagram" } ^ This WILL FAIL - Mermaid cannot parse descriptive text after syntax.

WHERE TO PUT STYLING:

• Visual preferences → "stylePreferences" parameter

• Colors → "brandColors" parameter

• Project context → "projectContext" parameter

• NOT in "prompt" when using diagram syntax

OUTPUT STYLES:

• "draft" - Fast rendering, minimal processing

• "standard" - AI-enhanced with brand colors (recommended for diagrams)

• "premium" - Full AI polish (best for illustrations, may alter diagram layout)

CROPPING OPTIONS:

• autoCrop: true - Automatically remove transparent edges

• Manual: cropX1, cropY1, cropX2, cropY2 - Precise pixel coordinates

Refine, modify, or create variations of an existing generated image.

Use this to:

• Edit specific parts of an image ("change the background to blue", "add a title")

• Apply style changes ("make it more minimalist", "use darker colors")

• Fix issues ("remove the text", "make the icon larger")

• Crop the image to specific coordinates

For diagram iterations:

1. Include the original Mermaid/D2/Vega source in your prompt to preserve structure

2. Be explicit about visual issues (e.g., "the left edge is clipped")

Retrieve detailed information about an image generation session and all its assets.

Returns:

• Session metadata (creation time, name, status)

• List of all assets with their prompts, toolchains, and status

• Parent-child relationships showing iteration history

Use this to:

• Review what was generated in a session

• Find asset IDs for iteration

• Understand the generation history and toolchains used

Export a generated image asset by session and asset ID.

Returns the image inline as base64 along with metadata (format, dimensions, size).

When running locally (stdio transport), you can optionally provide a destinationPath to save the image to disk. Files are only ever written inside one export folder (~/Downloads/opengraph-mcp, or MCP_EXPORT_DIR); pass a file name or a path inside it. SVG is returned as a file only, never inline, because it can carry script.

USAGE: After generating an image with generateImage, use the sessionId and assetId to export: exportImageAsset(sessionId="...", assetId="...")

To save to disk (local/stdio only): exportImageAsset(sessionId="...", assetId="...", destinationPath="logo.png")