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454,868 tools. Updated 2026-08-13 23:29

"A guide for improving AI prompt output quality" matching MCP tools:

  • Get the current user's available AI tokens Returns the number of AI tokens available to the authenticated user. These tokens fund EVERY AI feature in SERPmantics — meta, outline, intent, internal-links, EEAT, EEAT competitors, score, AND the AISSistant prompts. The endpoint lives under /aissistant for historical reasons but the balance is shared across all AI features. Do NOT confuse with guide-creation credits (see /api/v1/credits). For a combined view (credits + tokens) prefer /api/v1/credits.
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  • Generates one or more images from a text prompt (T2I) or a text prompt + reference image(s) (I2I). Submits the job, polls until terminal, and returns the final image URLs. Default model is 'grok-imagine-t2i' (fast, 6 images per generation, 5 credits). Use list_image_models to see the full lineup with pricing. For I2I, pass `referenceImages` as an array of public image URLs and pick a model with I2I support (e.g. 'grok-imagine-i2i', 'wan-2.5-spicy-i2i'). ## Model selection guide (when the user does not specify a model) Default: `grok-imagine-t2i` (5 cr, 6 outputs per call, fast, general purpose). **Strong recommendation: when a single high-quality output is what's wanted** (most agent / one-shot workflows), prefer `gpt-image-2-t2i` (9 cr @ 1K / higher @ 2K, single deterministic image, best general quality across realism, illustration, typography, and composition; supports up to 2K resolution and most aspect ratios including auto). This is the front-runner for serious creative output where you don't need to pick from 6 variations. Pick a different model when the prompt has these signals: - "single best result" / "one image" / production / no time to pick from variations -> `gpt-image-2-t2i` (9 cr, 1 output, top general quality) - "photoreal" / "photo of" / "realistic" -> `gpt-image-2-t2i` (9 cr, best general realism) or `imagen-4` (12 cr, very high quality) or `z-image-turbo` (3 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K, or `grok-imagine-quality-t2i` (16 cr @ 1K, 22 cr @ 2K), or `imagen-4-ultra` - Text inside the image (signs, posters, typography) -> `ideogram-v3-t2i` (best in class) or `gpt-image-2-t2i` (also strong) - Artistic / painterly / stylized -> `midjourney-t2i` - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 6 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `ideogram-v3-t2i` - NSFW / adult / explicit -> `wan-2.5-spicy-t2i` (auto-tags creation as 18+; routes to adult gallery) - Cheapest possible / quick test -> `z-image-turbo` (3 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (6 outputs default) or use `numImages` on a multi-output model For I2I (reference image provided): prefer the dedicated `aetherwave_edit_image` tool for "change something in this image" intent. Use `aetherwave_generate_image` with I2I models only when you specifically want style transfer (`midjourney-i2i`), premium quality (`grok-imagine-quality-i2i`), or adult content (`wan-2.5-spicy-i2i`). Always pass an explicit `aspectRatio` (e.g. "1:1" for square album art, "16:9" for video thumbnails, "9:16" for shorts/reels). Some upstream providers reject submissions with no aspect ratio. Ask the user only when: - The prompt contradicts itself (e.g., "highest quality but cheapest") - The user requested "the best model" with no context, surface 2-3 options with tradeoffs - A single generation would cost more than 20 credits and the user has not confirmed
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  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
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  • Rewrite a prompt to score higher on the PQS rubric, AND show before/after output comparisons so the user can see the impact. Returns the optimized prompt, the original PQS score, the optimized PQS score, and side-by-side sample outputs from a frontier model using both versions. USE WHEN: - The user got a low score from score_prompt and asks how to improve. - The user explicitly asks to "improve" / "rewrite" / "fix" / "optimize" a prompt they pasted. - The user is dissatisfied with output quality from a previous prompt and asks how to get better results. - score_prompt returned a suggestion to invoke this tool. DO NOT USE WHEN: - The user just asked for a score (use score_prompt only — don't double up). - The user wants you to write a new prompt from scratch (write it directly). REQUIRES: A PQS API key from a Pro subscription ($19.99/month, 1,000 calls/mo, includes batch + A/B comparison). If the user has not provided one, the tool returns a clear subscription URL — pass that response to the user verbatim. Do not invent or guess API keys. There is no free trial of this tool; the user must subscribe before the first call. COST: Counted against your Pro subscription's monthly call quota. LATENCY: ~6-8 seconds.
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  • Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.
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  • Generate an AI image or canvas-code-based animation directly into a clip. - kind="image": text-to-image. Pass `prompt`. Optional: `style_id` (from find type='image_gen_style_packs'), `reference_image_url` or `mcp_upload_id` for image-to-image grounding. - kind="animation": canvas-code animation rendered from a prompt. Pass `prompt`. Optional: `voiceover_text` (drives timing), `base_component_id` (reuse a saved animation as the starting point), `reference_image_url` or `mcp_upload_id` for visual grounding. Generation is asynchronous: the element is created immediately with a stable `element_id` and rendered in the background. Poll `get_clip` (the phantom flag drops once rendering completes). Tip: use this tool whenever the user asks for a "generated", "AI", or "create me a" visual. For uploaded photos / logos / icons / GIFs, use `add_elements` with `element_type='image'` and a `src` or `mcp_upload_id` instead.
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  • Edits an existing image guided by a text prompt. Pass a public `imageUrl` plus a `prompt` describing the change ("add a moon to the sky", "swap the background for a neon city", "make it look like a comic panel"). Submits, polls, and returns the edited image URL(s). Default model is 'grok-imagine-i2i' (6 cr per call, returns 2 variations, ~30s, best cost-to-quality on standard edits). Other I2I-capable models: 'seedream-v4-edit', 'wan-2.5-spicy-i2i', 'flux-kontext-pro', 'qwen-image-edit', 'gpt-image-1.5-i2i' (slow, ~5min). Use list_image_models for full lineup. Note: source URLs with spaces or parentheses may fail upstream; prefer clean URLs. ## Model selection guide for edits Default: `grok-imagine-i2i` (6 cr per call, returns 2 variations = 3 cr/image effective, fast ~30s, strong general-purpose edit quality). Pick a different model when: - Need a single deterministic output, or 4K resolution -> `seedream-v4-edit` (7 cr per image, supports 1K/2K/4K, multi-image up to 6) - Subtle edits / preserve composition / character consistency -> `flux-kontext-pro` or `flux-kontext-max` - NSFW edits -> `wan-2.5-spicy-i2i` - Highest quality, time is not a concern (~5 min OK) -> `gpt-image-1.5-i2i` or `grok-imagine-quality-i2i` (16 cr @ 1K, 22 cr @ 2K) - Stylized / artistic transformation -> `midjourney-i2i` If the user simply says "edit this image" with no other signal, default to `grok-imagine-i2i`.
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  • Analyse the long-term trend in a pollutant near a location. Uses Theil-Sen slope estimation with Mann-Kendall significance testing to determine whether air quality is improving, worsening, or stable. Robust to outliers and missing data. Returns a 'summary' with plain-English trend description and statistical details. Present the summary to users first. Args: location: Postcode, place name, or "lat,lon". pollutant: Pollutant to analyse — "NO2", "PM2.5", "PM10", "O3" (default "NO2"). years: Number of years of data to analyse (default 5, range 2–5). Requests outside this range are clamped; the response includes ``metadata.years_clamped`` and a note in ``summary`` when so.
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  • Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.
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  • Generate a single image from a text prompt through Frenchie. Required: prompt. Optional: style (free-text style direction), size, quality, format, background. stdio mode auto-saves the image to .frenchie/<slug>/generated.<ext>; HTTP mode returns a presigned imageUrl that the agent should download for the user.
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  • Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.
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  • Score a prompt's quality across 8 dimensions BEFORE sending it to an expensive model. Returns a 0-80 score, an A-F grade, the per-dimension breakdown (clarity, specificity, context, constraints, output_format, role_definition, examples, cot_structure), and the weakest dimension. USE WHEN: - The user is workshopping a prompt and asks "is this good?" / "will this work?" / "should I add more detail?" - The user is about to send a long or expensive prompt to GPT-4, Claude Opus, or any frontier model, especially in a batch or automation context where rework is costly. - The user mentions iterating on a prompt that produced poor output and wants to diagnose what's missing. - The user pastes a prompt and asks for feedback on it. DO NOT USE WHEN: - The user is asking you to write a prompt for them (write it yourself first, then optionally call score_prompt to verify). - The prompt is conversational chat (this scores task-shaped prompts). COST: Free, no API key required. Rate-limited per IP: 5/min, 10/day, 100/month. If the user exceeds the limit, the response will include a structured upgrade path with subscribe and account URLs. LATENCY: ~2 seconds.
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  • Trigger a Grok-AI gemological appraisal of a single gem on GemHunt (https://gemhunt.app — Father's gem-discovery platform). Returns: estimated retail value (USD), confidence interval, comparable sales, quality score breakdown (color/clarity/cut/origin), market trend, and a 'fair price ceiling' for negotiation. Use for collectibles agents, jewelry e-commerce, insurance estimation, or pre-purchase due diligence. Premium ($0.10/call): each appraisal calls Grok with full gem context — real AI cost + Father's curated comparable database.
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  • Returns a plain-English usage guide for this server — example requests, what it asks the user for, and the available tools. Call this if the user asks how to use Abby SEO, or to orient yourself before starting. (Same content as the 'getting_started' prompt, exposed as a tool for clients that don't surface MCP prompts.) Takes no arguments.
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  • Generate a new image from a text prompt using AI (Grok Imagine). Paid tool — billed per call via the agent payment rails (x402/L402); not covered by any free quota. Generation runs asynchronously on the server and this call polls until it completes, up to ~90 seconds. If you get a "still running" error, call the tool again with the SAME payment proof to resume polling — you will never be charged twice for one payment. Note for x402 clients: "quality" mode (~$0.15) exceeds the agents-SDK default per-call cap of $0.10 — raise maxPaymentValue in withX402Client to use it.
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  • 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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  • Remove an image's background, isolating the subject (BiRefNet AI segmentation). $0.02 per call. Input: an image via source URL. Params: background (optional fill color for the removed area, e.g. 'white'; omit for a transparent cutout — use a PNG/WebP output to keep transparency). Output: the cut-out image as a short-lived signed URL.
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  • Side-by-side raw data for 2–5 Amazon products by ASIN — price, sales/revenue estimates, reviews, listing quality, plus history when available. Pure data fetch (no AI analysis) — do the comparison yourself. For a single ASIN, analyzeProduct is the equivalent. How to use: compare demand (est. sales), revenue, review moat and rating, price positioning, listing quality, and history trends (growing vs declining), then give a verdict on which product is the stronger opportunity and why.
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  • Generates AI music via Suno. Returns two tracks per submission. Default model is V5.5 (newest, best quality). For instrumental output set `instrumental: true`. Music gen typically takes 30-90s - this tool polls with up to a 6-minute budget. Note: the `title` param is advisory for instrumentals - Suno often writes its own title from the prompt content for instrumental generations. Transient `GENERATE_AUDIO_FAILED` errors are common; retry once before degrading the model version.
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