Skip to main content
Glama
298,925 tools. Last updated 2026-07-14 17:50

"namespace:io.github.krakonjac300-pixel" matching MCP tools:

  • 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.
    Connector
  • 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`.
    Connector
  • 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`.
    Connector
  • 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`.
    Connector
  • Recover the native pixel grid from enlarged, softened, AI-rendered, or compressed pixel art. Standard uses the native Rust detector. Neural uses the neural reconstruction engine and accepts optional target width and height values. This repairs existing art—it does not generate a new image. Both engines are free and share a limit of 10 requests per minute per API token. Successful response JSON is limited to 850,000 bytes for AWS ALB compatibility.
    Connector
  • 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.
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Generate authentic pixel art - sprites, animations, and tilesets - from any MCP client

  • Hosted MCP server for Retro Diffusion. Generate authentic, grid-aligned pixel art — sprites, characters, animations, and tilesets — from Claude, Cursor, VS Code, Windsurf, or any MCP client. 90+ styles, free cost estimation, pay-per-generation with no subscription and credits that never expire.

  • 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.
    Connector
  • 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.
    Connector
  • 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`.
    Connector
  • 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.
    Connector
  • Generate images using the public /v1/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__*); they support reference_images for character/style consistency. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running. Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.
    Connector
  • Start a generation as an async job (POST /v1/inferences with async=true) and return a task_id. Recommended for advanced animations (rd_advanced_animation__*), other animation styles, and batches — they run for tens of seconds and can outlive a synchronous MCP call. Poll the returned task_id with get_inference_job roughly every 2-5 seconds.
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • 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.
    Connector
  • Return sensor dimensions, pixel pitch, and resolution for lens field of view, M12 lens, and C-mount lens matching inputs. In production this uses the read-only live sensor table when configured, with fixture fallback when unavailable. Use these specs as inputs to calculate_field_of_view or match_lens_to_sensor, not as a reason to hand-calculate FoV. FoV rule: never estimate sensor-specific FoV from catalog fields; use calculate_field_of_view or match_lens_to_sensor.
    Connector
  • Generate images using the public /v1/inferences endpoint. For the highest quality prefer RD Pro styles (rd_pro__*); they support reference_images for character/style consistency. For animation styles prefer start_inference_job + get_inference_job instead — animations are long-running. Use `input_image` for the main source image, `reference_images` for extra per-inference guidance, and `style_reference_images` only on create_user_style/update_user_style. The response excludes raw base64 image payloads to keep MCP outputs compact.
    Connector
  • Start a generation as an async job (POST /v1/inferences with async=true) and return a task_id. Recommended for advanced animations (rd_advanced_animation__*), other animation styles, and batches — they run for tens of seconds and can outlive a synchronous MCP call. Poll the returned task_id with get_inference_job roughly every 2-5 seconds.
    Connector
  • 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'.
    Connector