"A resource for finding specific high-resolution images online" matching MCP tools:

• token_ohlcv

  Nansen

  Get OHLCV (Open, High, Low, Close, Volume) price data for a token with automatic interval resolution. Supports EVM chains and Solana for on-chain tokens, AND Hyperliquid perpetual futures. For Hyperliquid perps, pass `chain="hyperliquid"` and use the perp symbol as `tokenAddress` (e.g. "BTC", "HYPE" for native perps; "XYZ:ORDI" for XYZ-namespaced perps — prefix is normalized automatically). **YOU MUST USE THIS** over `general_search` to get prices. **`general_search` prices are delayed and often incorrect.** To get **LATEST** price set from to '5MIN_AGO' and to to 'NOW'. Resolution is automatically calculated based on the date range - < 6 hours: 5 minutes - 6 hours - 1 day: 15 minutes - 1-3 days: 30 minutes - 3-7 days: 60 minutes (1 hour) - 7-90 days: Daily - 90+ days: Weekly Columns returned: - **Interval Start**: Timestamp of the start of the interval (datetime: YYYY-MM-DD HH:MM:SS) - **Open**: Opening price of the interval - **High**: Highest price of the interval - **Low**: Lowest price of the interval - **Close**: Closing price of the interval - **Volume USD**: Volume in USD of the interval Additional columns (when includeMarketCap=true): - **Open Market Cap**: Opening market cap in USD - **Close Market Cap**: Closing market cap in USD - **High Market Cap**: Highest market cap in USD - **Low Market Cap**: Lowest market cap in USD Example Usage: Get OHLCV for WETH over the past week (auto-resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "7D_AGO", "to": "NOW" } } ``` Get OHLCV for WETH over 30 days (will use daily resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "30D_AGO", "to": "NOW" } } ``` Get OHLCV for WETH for last 20 minutes (will use 5 minute resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "20MIN_AGO", "to": "NOW" } } ``` Get OHLCV for the BTC Hyperliquid perp over 7 days: ``` { "chain": "hyperliquid", "tokenAddress": "BTC", "date": { "from": "7D_AGO", "to": "NOW" } } ```

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• find_similar_products

  market

  Given a product ID, find similar products across the entire catalog. Useful for "more like this" recommendations or finding alternatives. Returns compact product cards, not full variant detail; call get_product for SKU-level variants, exact variant prices, merchant description, store info, and all images. Returns page and hasNextPage. Returns up to 10 results per page, paginated (max 3 pages).

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• upload_file

  BorealHost

  Upload a base64-encoded file to a site's container. Use this for binary files (images, archives, fonts, etc.). For text files, prefer write_file(). Requires: API key with write scope. Args: slug: Site identifier path: Relative path including filename (e.g. "images/logo.png") content_b64: Base64-encoded file content Returns: {"success": true, "path": "images/logo.png", "size": 45678} Errors: VALIDATION_ERROR: Invalid base64 encoding FORBIDDEN: Protected system path

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• token_ohlcv

  Nansen

  Get OHLCV (Open, High, Low, Close, Volume) price data for a token with automatic interval resolution. Supports EVM chains and Solana for on-chain tokens, AND Hyperliquid perpetual futures. For Hyperliquid perps, pass `chain="hyperliquid"` and use the perp symbol as `tokenAddress` (e.g. "BTC", "HYPE" for native perps; "XYZ:ORDI" for XYZ-namespaced perps — prefix is normalized automatically). **YOU MUST USE THIS** over `general_search` to get prices. **`general_search` prices are delayed and often incorrect.** To get **LATEST** price set from to '5MIN_AGO' and to to 'NOW'. Resolution is automatically calculated based on the date range - < 6 hours: 5 minutes - 6 hours - 1 day: 15 minutes - 1-3 days: 30 minutes - 3-7 days: 60 minutes (1 hour) - 7-90 days: Daily - 90+ days: Weekly Columns returned: - **Interval Start**: Timestamp of the start of the interval (datetime: YYYY-MM-DD HH:MM:SS) - **Open**: Opening price of the interval - **High**: Highest price of the interval - **Low**: Lowest price of the interval - **Close**: Closing price of the interval - **Volume USD**: Volume in USD of the interval Additional columns (when includeMarketCap=true): - **Open Market Cap**: Opening market cap in USD - **Close Market Cap**: Closing market cap in USD - **High Market Cap**: Highest market cap in USD - **Low Market Cap**: Lowest market cap in USD Example Usage: Get OHLCV for WETH over the past week (auto-resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "7D_AGO", "to": "NOW" } } ``` Get OHLCV for WETH over 30 days (will use daily resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "30D_AGO", "to": "NOW" } } ``` Get OHLCV for WETH for last 20 minutes (will use 5 minute resolution): ``` { "chain": "ethereum", "tokenAddress": "0xba5ddd1f9d7f570dc94a51479a000e3bce967196", "date": { "from": "20MIN_AGO", "to": "NOW" } } ``` Get OHLCV for the BTC Hyperliquid perp over 7 days: ``` { "chain": "hyperliquid", "tokenAddress": "BTC", "date": { "from": "7D_AGO", "to": "NOW" } } ```

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• erase

  cnvs.app

  Delete a single item by id. `kind` MUST match the item type: 'text' for text nodes, 'line' for freehand strokes, 'image' for images — the wrong kind silently targets the wrong table and is a common mistake. Get the id + type from `get_board` (texts[], lines[], images[]). There is no bulk/erase-all tool: loop if you need to delete multiple items.

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• create_edit_session

  mcp

  Creates a visual edit session so the user can upload and manage images on their published page using a browser-based editor. Returns an edit URL to share with the user. When creating pages with images, use data-wpe-slot placeholder images instead of base64 — then create an edit session so the user can upload real images.

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• emem_grid_info

  emem — Earth memory protocol

  Active grid encoding: cell64 ground resolution, lat/lng axis sizes, DGGS lineage. When to use: Call once at session start (or when the user asks about cell resolution / 'how big is a cell'). Returns the actual ground resolution today (~9.54 m × 9.55 m square at the equator (lat 21 bits × lng 22 bits, matching Sentinel-1/Sentinel-2 native pixel pitch). The cell64 bit layout reserves a resolution-tag field for future hierarchical refinement targeting H3-equivalent res-13 (~3.4 m) cells in v0.1.) and the spec target. Useful before you reason about whether one cell is enough or whether you need `emem_recall_polygon`.

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• search_datasheets

  sheetsdata-mcp

  Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.

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• analyze_image

  sheetsdata-mcp

  Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')

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• audit_dependencies

  proof-of-commitment

  Batch-score multiple npm or PyPI packages for supply chain risk. Takes a list of package names and returns a risk table sorted by commitment score (lowest = highest risk first). Risk flags: - CRITICAL: single npm publisher + >10M weekly downloads (publish-access concentration risk) - HIGH: new package (<1yr) + high downloads (unproven, rapid adoption = supply chain risk) - WARN: low publisher count + high downloads Perfect for auditing a full package.json or requirements.txt — paste your dependency list and get a prioritized risk report. Examples: score all deps in a project, compare two similar packages, identify abandonware before it becomes a CVE.

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• get_board

  cnvs.app

  Full structured JSON state of a board: texts (id, x, y, content, color, width, postit, author), strokes (id, points, color, author), images (id, x, y, width, height, dataUrl, thumbDataUrl, author; heavy base64 >8 kB elided to dataUrl:null, tiny images inlined). Use this for EXACT ids/coordinates/content (needed for `move`, `erase`, editing a text by id). For visual layout (where is empty space? what overlaps?) call `get_preview` instead — it's much cheaper for spatial reasoning than a huge JSON dump.

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• render_slides

  Slidev MCP

  Render a Slidev presentation from markdown and return its hosted URL. IMPORTANT: Before calling this tool, you MUST call get_theme with the theme name you plan to use. Each theme has unique layouts, components, and frontmatter options. Apply the theme's specific features in your markdown to produce high-quality slides that match the theme's design. If the user has not specified a theme, call list_themes to pick one. If you are unfamiliar with Slidev markdown syntax, call get_slidev_guide. Images must be remote URLs or base64-encoded inline. Local file paths are not supported.

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• search_content

  humanMCP — kapoost

  Search across all kapoost's pieces — poems, essays, notes, images. Matches query against title, body, tags, and description. Returns matching pieces with a preview snippet. Use this instead of reading every piece when looking for specific themes, words, or topics.

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• upscale_image

  Sats4AI - Bitcoin-Powered AI Tools

  Upscale images 2x or 4x with neural super-resolution. Uses Real-ESRGAN (ICCV 2021, PSNR 32.73dB on Set5 4x, 100M+ production runs). Recovers real detail from low-resolution images — not interpolation. Optional face enhancement. Stable endpoint — model upgrades automatically as SOTA evolves. 5 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='upscale_image'.

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• org.health

  Koalr

  Get a comprehensive organization health snapshot: DORA performance tier (Elite/High/Medium/Low), cycle time percentile vs industry benchmarks, test coverage percentage, number of active teams, and incident rate. Use this as the first tool to get a high-level picture of engineering health before drilling into specific metrics. Read-only.

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• health_check

  discava – Business Directory for AI

  Check if the discava API is online and responding. Returns status and version. No parameters required.

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• workflows_run

  Roboflow (Official)

  Execute a saved Workflow on one or more images. Runs a previously created Workflow against the provided images on the Roboflow serverless infrastructure. IMPORTANT: If processing more than 10 images, spawn a sub-agent to run this tool in the background so the user is not blocked. Returns workflow outputs as defined by the workflow's output blocks.

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• get_theme

  Slidev MCP

  Get full documentation for a specific theme: layouts, components, and examples. Call this BEFORE render_slides to learn the theme's unique features. Each theme has different layouts, components, and frontmatter options. Use what you learn here to produce high-quality, theme-specific slides. This is the primary tool for preparing to render slides. When the user specifies a theme, call this directly — no need to call browse_themes.

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• models_infer

  Roboflow (Official)

  Run hosted inference on an image using a trained model. Returns JSON predictions only. For visualized/annotated images, use workflow_specs_run with a visualization block instead.

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• get_device

  Trillboards DOOH Advertising

  Get detailed information about a specific device. WHEN TO USE: - Checking status of a single device - Getting device configuration details - Debugging device issues RETURNS: - device_id: Your internal device ID - trillboards_device_id: Internal Trillboards ID - fingerprint: Device fingerprint - name: Device name - status: online/offline - last_seen: Last heartbeat timestamp - location: Location details - specs: Device specifications - stats: Impression and earnings stats EXAMPLE: User: "Get details for vending machine 001" get_device({ device_id: "vending-001-nyc" })

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