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650,521 tools. Updated 2026-10-14 00:42

"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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  • Generate a video post (postType VIDEO) using PostNitro's AI engine. Returns an embedPostId to track progress. The content is AI-generated from your aiGeneration prompt — you do NOT provide slides. Each slide the AI writes becomes a scene in the video. (To supply your own scene content instead, use postnitro_import_video.) Output: 'DESIGN' (default) creates the design without rendering; 'MP4' renders the video file and requires videoSettings (duration, optional audio track). templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults. Otherwise provide them here (use the list tools to find valid IDs). Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_generate_video_and_wait for one step.
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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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  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
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Matching MCP Connectors

  • Convenience tool: AI-generates a video post (postType VIDEO), polls until completion, and returns the final output. Rendering a video takes longer than a carousel — typically 15-45 seconds when responseType is 'MP4', and longer for designs with animations or GIFs (which use the enhanced renderer). The content is AI-generated from your aiGeneration prompt — you do NOT provide slides. (To supply your own scene content instead, use postnitro_import_video_and_wait.) Output: 'DESIGN' (default) creates the design without rendering; 'MP4' renders the video file and requires videoSettings. templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults.
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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, 2 images per generation, 6 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` (6 cr, 2 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), `nano-banana-2` (8 cr @ 1K), or `z-image-turbo` (2 cr, fastest) - "highest quality" / "premium" / no budget -> `gpt-image-2-t2i` at 2K (12 cr), or `grok-imagine-quality-t2i` (14 cr) - Text inside the image (signs, posters, typography) -> `gpt-image-2-t2i` (strongest lettering of the live models) - Album art / cover art -> `gpt-image-2-t2i` for one strong image; `grok-imagine-t2i` for 2 variations to choose from; `seedream-v4-t2i` if 4K wanted - Logo or design with embedded text -> `gpt-image-2-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` (2 cr) - Multiple variations to compare -> keep `grok-imagine-t2i` (2 outputs per call) 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, 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 Returns within ~45 s. If the result has state "running" the job is still rendering: poll aetherwave_get_job with the returned taskId and kind every ~10 s. Pass wait:true ONLY if your MCP client permits tool calls longer than 60 s (most do not). Pass async:true to get the taskId back immediately without waiting.
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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' (10 cr, one image, ~15-30s, 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` (10 cr, one image, fast ~15-30s, 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` (6 cr medium, 31 cr high) or `grok-imagine-quality-i2i` (14 cr) If the user simply says "edit this image" with no other signal, default to `grok-imagine-i2i`. Returns within ~45 s. If the result has state "running" the job is still rendering: poll aetherwave_get_job with the returned taskId and kind every ~10 s. Pass wait:true ONLY if your MCP client permits tool calls longer than 60 s (most do not). Pass async:true to get the taskId back immediately without waiting.
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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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  • 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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  • Textured 3D model (GLB) from an image or a text prompt. texel-3d-pro = state of the art, highest quality, HD texture with PBR maps (base color, metal/roughness, normal); texel-3d-alt = another excellent option, PBR maps; texel-3d-express = fast, decent quality (~20K triangles, PBR); texel-3d-draft = very fast drafts and distant props, color texture only, not for final quality. Without image_url, a reference image is generated from the prompt first. Asynchronous: returns a generation id; poll get_generation. Spends credits (refunded automatically on failure). Typical time: 180 s.
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  • Calculation aid for bid evaluation: computes the score and ranking of bids under the chosen UfAB scoring method — einfach (L/P ratio), erweitert (leading group) or gewichtet (weighted normalisation) — plus a sensitivity analysis showing how far the price of the top-ranked bid can rise before the ranking changes (§ 127 GWB, § 58 VgV; UfAB-Richtwertmethoden Stand 2016). Input: bid prices and existing quality scores; output: the arithmetic ranking. Evaluating quality and the award decision itself remain with the contracting authority. `methode`: percentage weights for price and quality (e.g. 'Preis 40 %, Leistung 60 %') correspond to gewichtet with gewichtLeistung=60; without weights, best quality per euro corresponds to einfach (Leistung/Preis ratio); close bids decided by a leading group plus a tie-break criterion correspond to erweitert. One method per call (default einfach). The tool scores bids; it does not assess quality, so quality scores have to exist already. Key output fields: result.ergebnis.sieger (top-ranked bid), result.ergebnis.zeilen[] (ranking with kennzahl and rang), result.sensitivitaet, legal_basis. Example user questions: "Welches Angebot gewinnt nach Preis-Leistungs-Verhältnis?"; "Wertungsmatrix nach UfAB berechnen"; "Zuschlag auf das wirtschaftlichste Angebot ermitteln"; "Sensitivitätsanalyse der Angebotswertung"; "Führungsgruppe nach erweiterter Richtwertmethode"
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  • Free. Returns the complete three-source intelligence report for a fixed sample token (BONK), exercising the exact production pipeline — verify output quality before paying for token_intel.
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  • Fetch one prompt in full by slug (or UUID): complete prompt text, structured metadata (model, prompt type, style, quality assessment), author, media and source attribution. Optional locale (es, pt, fr, zh, de, ja) returns the translated version.
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  • Request a translation for supplied text and a target language. May use stored data or AI generation; language coverage and quality vary. Requires a Word Orb key and applicable plan entitlement; counts toward account quotas. Use lookup_words to inspect only existing translations.
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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. Price: $0.08 standard or $0.20 quality, paid in sats over Lightning at the current BTC/USD rate or in USDC on Base. Note for x402 clients: "quality" mode exceeds the agents-SDK default per-call cap of $0.10 - raise maxPaymentValue in withX402Client to use it.
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  • Approve a draft and hand it to the campaign executor for sending (respecting daily limits and allowed hours). Optionally pass edited_messages to overwrite specific drafts before approval. By default each draft is verified by a second-pass quality check (Czech vocative, hallucinations, prompt adherence) — set skip_gpt_check=true to bypass. If the quality check rejects the message, the draft is automatically sent back for a rewrite based on the feedback.
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  • Get a long completion with a large output budget, same engine as /v1/llm. Long-form LLM inference: same engine as /v1/llm with a four times larger output budget, up to 8000 tokens, for answers the cheap route truncates. Body: {prompt, system?, max_tokens?} — $0.020000/call, paid per request via x402 (USDC). Use when asked: "long completion with a large output", "write a long answer to this prompt", "generate up to 8000 tokens".
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