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231,124 tools. Last updated 2026-06-24 18:06

"namespace:io.github.adebayojuwon200-pixel" matching MCP tools:

  • Fetch a creator's posts, sorted and paginated. Use this when the user asks to see what a creator has posted (e.g., "show me Jane's last 20 posts", "what are this creator's top-engagement reels?", "pull recent posts from creator-id ABC"). Identify the creator by either `creator_id` (UUID) OR (`platform` + `username`). `sort` defaults to "recent" (newest first); use "top_engagement" for the highest- engagement posts, or one of "most_likes" / "most_views" / "most_comments" for a specific metric. `limit` defaults to 12 and is capped at 50. Pass `cursor` from a previous response's `next_cursor` to paginate. Returns post records (caption, media URL, like/comment/view counts, timestamps), plus `has_more` and `next_cursor` for pagination. Examples: - User: "Show @niickjackson's recent Instagram posts" -> use this tool with platform "instagram" and username "niickjackson". - User: "Is @niickjackson a fit for Pixel?" -> use this after `get_profile` when the fit analysis needs recent content evidence, then call `match_creators`.
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  • Convert HTML or Markdown to a pixel-perfect PDF. Returns JSON: { url } — a temporary download URL (valid ~1 hour). Great for generating invoices, reports, receipts, or formatted documents programmatically. Supports full HTML/CSS including tables, images (base64 or URL), and inline styles. For Markdown input, set format='markdown'. 50 sats per conversion. Use convert_file instead for converting existing files between formats (e.g., DOCX→PDF). Pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='convert_html_to_pdf'.
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  • Score how well specific creators fit a campaign brief or search intent. Use this when the user already has candidate creators in mind and wants to evaluate fit (e.g., "rate these 5 creators for a vegan cookbook launch", "which of these is the best match for my crypto audience?"). For each creator the API returns a match score (0-1), a good/neutral/avoid decision, and structured reasons. Pass candidates in `creator_ids` (canonical UUIDs) and/or `profiles` (platform + username). `intent_query` is the brief the LLM reasons against; `intent_context` is optional extra context (target audience, brand values, prior collabs). Use `semantic_search_creators` when you don't have candidates yet and need topical or niche discovery. Use `search_creators` first when you only need to resolve rough creator names/handles into candidates. Use `find_lookalike_creators` when you want creators similar to known good fits. Examples: - User: "Is @niickjackson a fit for Pixel?" -> use this tool after resolving the exact Instagram profile with `get_profile`; call `get_posts` first if recent content context is needed. - User: "Rate these five creators for a vegan cookbook launch" -> use this tool.
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  • 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
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  • Fetch a single social profile by (platform, username). Always use this first when the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram") and you need the full profile: bio, follower/engagement metrics, recent activity, growth, and the canonical creator ID. Pass exactly the username they typed without the @ sign — case-insensitive matching is handled server-side. Do not use `search_creators` for an exact platform+username lookup. Examples: - User: "Pull @niickjackson on Instagram" -> use this tool with platform "instagram" and username "niickjackson". - User: "Tell me about instagram.com/niickjackson" -> parse the platform and username, then use this tool. - User: "Is @niickjackson a fit for Pixel?" -> use this tool first, then call `get_posts` and/or `match_creators` if the task needs content or fit analysis. Returns the profile record plus the underlying creator record. If you already have a creator UUID, use `get_creator` instead. For batch lookups by handle, use `lookup_profiles`.
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  • Fetch a creator's posts, sorted and paginated. Use this when the user asks to see what a creator has posted (e.g., "show me Jane's last 20 posts", "what are this creator's top-engagement reels?", "pull recent posts from creator-id ABC"). Identify the creator by either `creator_id` (UUID) OR (`platform` + `username`). `sort` defaults to "recent" (newest first); use "top_engagement" for the highest- engagement posts, or one of "most_likes" / "most_views" / "most_comments" for a specific metric. `limit` defaults to 12 and is capped at 50. Pass `cursor` from a previous response's `next_cursor` to paginate. Returns post records (caption, media URL, like/comment/view counts, timestamps), plus `has_more` and `next_cursor` for pagination. Examples: - User: "Show @niickjackson's recent Instagram posts" -> use this tool with platform "instagram" and username "niickjackson". - User: "Is @niickjackson a fit for Pixel?" -> use this after `get_profile` when the fit analysis needs recent content evidence, then call `match_creators`.
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  • Read your team's end-of-day reports and roster from Eodly.

  • Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.

  • Use this when you need to trace features from a reference photo into waypoints. Trace pixel-space features from a reference photo into normalized [0..1] waypoints the agent can map to mm via a known scale anchor and feed to path().spline / path().nurbsSegment. Three backends are dispatched behind the scenes: `opencv` (deterministic; uniform-bg silhouette only), `vision-llm` (Claude vision; named points/cluttered backgrounds; caller-supplied ANTHROPIC_API_KEY), and `hybrid` (opencv silhouette + LLM-labeled named points). Default backend is `auto` — the tool picks based on the image's corner-color stddev. Accuracy honesty: opencv contour is geometrically exact; vision-LLM is typically 5–10% off on dense landmarks. Per-feature `confidence` is reported. Caller pays for any vision-LLM API spend via their own ANTHROPIC_API_KEY. Pair with the `kernelcad-trace-from-image` skill for the conversion-to-mm pipeline.
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  • Score how well specific creators fit a campaign brief or search intent. Use this when the user already has candidate creators in mind and wants to evaluate fit (e.g., "rate these 5 creators for a vegan cookbook launch", "which of these is the best match for my crypto audience?"). For each creator the API returns a match score (0-1), a good/neutral/avoid decision, and structured reasons. Pass candidates in `creator_ids` (canonical UUIDs) and/or `profiles` (platform + username). `intent_query` is the brief the LLM reasons against; `intent_context` is optional extra context (target audience, brand values, prior collabs). Use `semantic_search_creators` when you don't have candidates yet and need topical or niche discovery. Use `search_creators` first when you only need to resolve rough creator names/handles into candidates. Use `find_lookalike_creators` when you want creators similar to known good fits. Examples: - User: "Is @niickjackson a fit for Pixel?" -> use this tool after resolving the exact Instagram profile with `get_profile`; call `get_posts` first if recent content context is needed. - User: "Rate these five creators for a vegan cookbook launch" -> use this tool.
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  • Semantic discovery search for influencers/content creators using natural-language queries. Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria (e.g., "fitness creators in NYC", "vegan recipe creators with high engagement", "tech reviewers who cover phones"). The query is matched against creator profiles, extracted facts, and visual style via hybrid vector search. Do not use this for exact handles, usernames, or known creator names. If the user gives a specific platform and handle (for example "@niickjackson on Instagram"), use `get_profile` first. For rough name/handle lookup, use `search_creators`. For multiple known handles, use `lookup_profiles`. Semantic search can return lookalike or topical matches and is allowed to miss an exact username. Examples: - User: "Find news creators with 1M+ followers" -> use this tool. - User: "Find creators in LA who make cinematic travel videos" -> use this tool. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. - User: "Is @niickjackson a fit for Pixel?" -> use `get_profile` first, optionally `get_posts`, then `match_creators`. Returns a ranked list of creators (id, platform, username, follower count, engagement rate, top categories, evidence facts). Use the flat follower, engagement-rate, and verified fields to constrain results when the user gives concrete numeric constraints. Use `find_lookalike_creators` instead when you want creators SIMILAR to known ones. Use `match_creators` when you want to SCORE specific creators against a brief.
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  • Fetch a single social profile by (platform, username). Always use this first when the user gives an exact handle on a specific platform (for example "@niickjackson on Instagram") and you need the full profile: bio, follower/engagement metrics, recent activity, growth, and the canonical creator ID. Pass exactly the username they typed without the @ sign — case-insensitive matching is handled server-side. Do not use `search_creators` for an exact platform+username lookup. Examples: - User: "Pull @niickjackson on Instagram" -> use this tool with platform "instagram" and username "niickjackson". - User: "Tell me about instagram.com/niickjackson" -> parse the platform and username, then use this tool. - User: "Is @niickjackson a fit for Pixel?" -> use this tool first, then call `get_posts` and/or `match_creators` if the task needs content or fit analysis. Returns the profile record plus the underlying creator record. If you already have a creator UUID, use `get_creator` instead. For batch lookups by handle, use `lookup_profiles`.
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  • Where the visible bodies land in a framed photo of the sky, for an image prompt. Give a place, a moment, an aim (compass direction and altitude), a lens, and an image size; get each in-frame body's pixel position, apparent size, brightness, the Moon's phase orientation, a sky-state summary (twilight, limiting magnitude, horizon row), the bright bodies just outside the frame, a ready-to-use prompt, and a machine-readable `renderPlan` (a body-free background-plate prompt plus the computed layers to composite locally, for a hybrid render pipeline). Caelus computes the geometry and photometry; it does NOT render the image. For "at sunset", first find the set time with sky_events, then pass it as date.
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  • Remove the background from a single image, returning the subject isolated on a transparent background. Supply the source image (URL or base64); optionally set crop to trim the result to the content, and creative_edit (default true) for higher-quality output that may not match the input pixel-for-pixel. Synchronous: the call blocks and returns a single image result with a url (not an array). The image is uploaded and validated, and an image larger than 15MB is rejected with HTTP 400. Credits are charged only on success. Use removeBackground for this dedicated cutout task; editImage can also remove backgrounds via a prompt but is better for broader edits, while createImage and generateWithStyle produce new images rather than process an existing one. Pass an optional request_id to tag the result so you can retrieve it later via getImageResults. Requires an API key (user scope). Credits: This endpoint consumes 0.5 credits per result.
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  • 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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  • Semantic discovery search for influencers/content creators using natural-language queries. Use this only when the user asks to discover creators by topic, audience, geography, niche, content style, or campaign criteria (e.g., "fitness creators in NYC", "vegan recipe creators with high engagement", "tech reviewers who cover phones"). The query is matched against creator profiles, extracted facts, and visual style via hybrid vector search. Do not use this for exact handles, usernames, or known creator names. If the user gives a specific platform and handle (for example "@niickjackson on Instagram"), use `get_profile` first. For rough name/handle lookup, use `search_creators`. For multiple known handles, use `lookup_profiles`. Semantic search can return lookalike or topical matches and is allowed to miss an exact username. Examples: - User: "Find news creators with 1M+ followers" -> use this tool. - User: "Find creators in LA who make cinematic travel videos" -> use this tool. - User: "Pull @niickjackson on Instagram" -> use `get_profile`, not this tool. - User: "Is @niickjackson a fit for Pixel?" -> use `get_profile` first, optionally `get_posts`, then `match_creators`. Returns a ranked list of creators (id, platform, username, follower count, engagement rate, top categories, evidence facts). Use the flat follower, engagement-rate, and verified fields to constrain results when the user gives concrete numeric constraints. Use `find_lookalike_creators` instead when you want creators SIMILAR to known ones. Use `match_creators` when you want to SCORE specific creators against a brief.
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  • Capture a real pixel screenshot of the user's tab. Unlike the DOM/style tools, this returns what the browser actually painted — true fonts, cross-origin images, shadows, gradients, blend modes. Use it for design review and visual-fidelity checks the DOM can't answer. Requires the user to have ticked "Allow screenshots" on the consent screen AND to have clicked "Share" on the sncro badge to start the screen-share (a one-time per-session browser prompt). If you get SCREENSHOTS_NOT_CONSENTED, ask the user to create a new session with that box checked. If you get SCREENSHOTS_NOT_STARTED, ask them to click "Share" on the sncro badge in the page corner. Args: key: The sncro session key secret: The session secret from create_session max_width: Max image width in px; the frame is scaled down to fit (default 1280)
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  • One retrieval, auto-picked substrate — the MCP twin of HTTP POST /v1/retrieve with substrate="auto". Runs a single post-cutoff retrieval, then returns whichever delivery substrate is cheapest AND legible for your `reader` model's token billing: - text — raw result pieces (Claude/GPT pixel billing, or any unknown reader). - glyph — a dense photo-glyph image (Gemini/Qwen flat-tile billing) you read with vision; the raw pieces ride along as a citation index. - answer — a pre-cited synthesized paragraph (weak tool-callers; needs a server LLM key). The trailing JSON block always carries a `selection` object {substrate, reader, reader_class, tier, rationale, estimates} so the choice is auditable from the honest token math — the same object the HTTP route returns. When the pick is glyph, the page image(s) precede that JSON block. Pricing matches /v1/retrieve: text/glyph bill the flat /query rate, answer bills the answer rate. The answer rate is charged up front and the delta is refunded when the pick resolves to text/glyph, so you always pay exactly the right rate.
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  • Generate a custom avatar SVG. Specify a style (e.g., 'avataaars', 'pixel-art', 'lorelei') and seed (e.g., username). Returns the SVG URL ready to display.
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  • Return all available CC0 images for a Smithsonian object at multiple resolutions. Only CC0 (open access) images are returned — throws Forbidden when an object has media but none of it is CC0. Each image entry includes thumbnail (~120px), screen-size (~800px), and high-resolution JPEG/TIFF URLs with pixel dimensions. Use smithsonian_search with filters.cc0_only to find CC0 objects before calling this tool.
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  • Detects the true image format of any URL via magic byte inspection — works even when the file extension or Content-Type header lies (common with proxied or CDN-hosted images). Returns: format (png/jpeg/gif/webp/avif/bmp/tiff/svg/ico/unknown), detected MIME type, whether Content-Type header matches, file size (bytes), and pixel dimensions for PNG and JPEG. No API key required. $0.050/call.
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  • List the Meta ad accounts connected to this Adbloop user, with the Page NAME + Instagram username + pixel each account uses. Use this to resolve a spoken account/page name to IDs.
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