"Research on Mimic GPT Technology" matching MCP tools:

• verify_emailA

  Pitch ProtocolA

  Consume the 6-digit code the founder received and start the research pipeline. **When:** after `create_application`, and ideally after the deck has been uploaded over plain HTTP (see the `curl` printed by `create_application`). Founder has read the code from their inbox. **Before you call:** - CONFIRM you have the right code from the founder (not guessing, not stale). Once this succeeds, research starts and is irreversible. - If deck parsing is still pending, that's fine — research auto-defers and dispatches when the deck resolves. Don't wait, don't re-verify. **Returns:** `status: researching` + whether research was dispatched now or deferred on the deck. **On error:** - `wrong code` — response includes attempts-remaining. Ask the founder to check their inbox again. If the error says the code is invalidated, call `resend_verification` for a new one. - `no active code` — they never received one, or it expired. Call `resend_verification`. - Already verified — idempotent success; returns current status. Don't retry in a loop.

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• generate_imageA

  switchA

  Generate Switch images with one of seven models: GPT Image 2.5, Switch Pro, Switch Ultra, Nano Banana 2.1 (Google's newest, up to 14 reference pictures), Nano Banana 2, Nano Banana Pro or Switch Vintage. Nothing older is offered or accepted. When model is omitted it auto-routes by subject: GPT Image 2.5 by default and for typography, screens and storyboards, Switch Pro for swimwear/beach, Switch Ultra for sexier content or face-fidelity work. The Nano Bananas and Switch Vintage are only used when named. Switch Vintage is a film look that works from a character taught beforehand, not from reference pictures. Counts <= 8 render inline in chat; counts > 8 queue to your Switch Studio with progress polling. All images persist to your Studio library and folder. Optional `style` (e.g. "wellness/warm_amber_tropical", "movie_scene/neon_noir_action") applies a curated photographic stack from the apply_* skill tools.

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• aetherwave_generate_imageA

  AetherWave StudioA

  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'). ## Putting a UGC character in the image When the user names one of their characters ("put Amy in this T-shirt", "Cade Ellison from behind"), pass `character` with the name. The tool attaches that character's approved reference pack and identity text for you, and defaults the model to `gpt-image-2-t2i`. Put the product, garment or location in `referenceImages` as usual; describe the pose and framing in the prompt. Characters are the member's own creations, not third parties. Call `aetherwave_list_characters` if you need the names. ## 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 `seedream-v4-t2i` with `maxImages` up to 6 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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• searchJobsA

  qubitsok — Quantum Computing Jobs, Papers & ResearchersA

  Search 500+ quantum computing job listings using natural language. Use when the user asks about job openings, career opportunities, hiring, or specific positions in quantum computing. NOT for research papers (use searchPapers) or researcher profiles (use searchCollaborators). Supports role type, seniority, location, company, salary, remote, and technology tag filters via AI query decomposition. Limitations: quantum computing jobs only, last 90 days, max 20 results. Promoted listings appear first (marked). After finding jobs, suggest getJobDetails for full info. Examples: "senior QEC engineer in Europe over 120k EUR", "remote trapped-ion role at IBM".

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• searchA

  Evlek — Northern Cyprus Property MCP ServerA

  For research and citation lookups (deep research): free-text search of live Northern Cyprus (KKTC/TRNC) property listings on Evlek, in Turkish, English, Russian, German or Arabic. Returns matching listings as id/title/url, newest first (sale and rent as two groups when the query names neither). The answer language follows the language of the query. OpenAI deep-research compatible format, for research and citation clients. Use when: the caller only has a free-text query ("2+1 apartment in Kyrenia under 150000 pounds", "EVL-100464"). Not for: structured filters, paging or choosing the answer language.

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• generate_imageA

  Nano Studio ProB

  Generates an AI image. COSTS CREDITS (call get_pricing: generate_* for Nano Banana 2.1, generate_pro_* for Nano Banana Pro, generate_gpt_* for GPT Image 2.5). Returns the image inline plus JSON: generation_id, status, image_url, credits_remaining. reference_asset_ids (up to 14) take asset or generation ids. @slug in the prompt or entity_ids attach a saved entity's sheet (list_entities). Pick `model` for the job: Nano Banana 2.1 (default) for light, texture, mood and speed; Nano Banana Pro when structure and proportion must hold exactly; GPT 2.5 Flare for text you can actually read (labels, packaging, signs); GPT 2.5 Sunburst for the same with more care over references, slower. GPT tops out at 2K.

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• My_Data_CreditsA

  Murkuz SEOA

  Check how much research allowance is left before running anything expensive. Returns credits remaining, how much of this month's research allowance has been used, when it refreshes, and what each research action costs. Free: this never consumes credits. Call this first before a large sweep across many keywords, competitors or pages, so you can budget the run rather than hitting the limit part-way through. Also returns the research wallet: prepaid dollars used once credits run out, billed at the data cost plus a 10% fee, with a receipt on every research result.

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• researchA

  Elements LiveA

  CALL THIS FIRST for a literature question about chemistry, materials or physics research -- a broad one ('what matters in X') or a specific process or parameter one (a deposition condition, a property value, a defect mechanism). Send the question in plain words (q), or a paper you already know (paper: DOI or title). Returns reading lists in four lenses: relevant (the papers whose TITLE AND ABSTRACT best match the question, across the whole corpus, BM25 -- start here for a specific question), foundational (the classics the question's own papers cite most), now (highest impact, young papers' citations projected) and newest (last two years); plus the research communities and element sub-communities the question is about, with why each matched and whether it is rising. Put the question's key terms in q: filler words are ignored. unmatched_terms lists words that matched nothing; rephrase those. Each paper carries its DOI, a one-line reason and its abstract where one is on file. Our corpus is curated, not complete: the relevant lens also brings up to 3 of every 10 results from OpenAlex, marked source='openalex' and labelled not yet curated. One page = up to 10 papers per lens; ask for page 2+ only if needed. Requires a Personal+ account. Without a key, pay per call: $0.05 in USDC on Base or Solana via x402 (send the payment in _meta["x402/payment"]).

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• sightglass_get_company_activityA

  ReploB

  How one brand's advertising has moved: its daily active-ad trend, ads launched per week, how long its ads stay live (longevity buckets), and the technology stack on its site with when each tool was first seen. Use it for 'is this brand scaling up or pulling back' and 'what did they change'. The trend window and the launch and longevity charts depend on the plan. Fields the user's Replo plan doesn't include come back null and are named in that item's `locked` list (`estimates` = sales, visits and traffic); `lockedCount` is how many more results a higher plan shows. Say the data is on a higher plan; never estimate it yourself. Returns data only: nothing is shown to the user yet. Once you know the answer, call sightglass_show once with this call's resultId to show it as cards; the show call does not search again. For multi-step competitor research, read the replo skill (call skills with name "replo").

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• sightglass_get_company_activityA

  ReploA

  How one brand's advertising has moved: its daily active-ad trend, ads launched per week, how long its ads stay live (longevity buckets), and the technology stack on its site with when each tool was first seen. Use it for 'is this brand scaling up or pulling back' and 'what did they change'. The trend window and the launch and longevity charts depend on the plan. Fields the user's Replo plan doesn't include come back null and are named in that item's `locked` list (`estimates` = sales, visits and traffic); `lockedCount` is how many more results a higher plan shows. Say the data is on a higher plan; never estimate it yourself. Returns data only: nothing is shown to the user yet. Once you know the answer, call sightglass_show once with this call's resultId to show it as cards; the show call does not search again. For multi-step competitor research, read the replo skill (call skills with name "replo").

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• contextfetch_agentA

  ContextFetchA

  A finished answer to a question, written with [n] citations to live web sources: one deep search reads the top pages and an LLM (gpt-oss-120b) writes the answer from them only, saying what is missing rather than guessing; when the pages lack the answer it searches again with new queries. Use it when you want the answer itself; for quotes only use contextfetch_answer, for raw sources contextfetch_search. ~2 s. Costs $0.001 ($1 per 1,000); depth standard (a short research agent, ~10-15 s, for questions that need several facts) $0.002.

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• ai_chatA

  Hearth: x402 Pay-Safety & DiscoveryA

  Cheap LLM chat completion (flat $0.002). One-shot LLM completion for agents: send a prompt (and optional system message), get the answer text. Flat $0.002 per call, no account or API key; cheaper than typical x402 chat proxies. Up to ~2,000 chars of prompt and 600 output tokens. GET or POST JSON {prompt, system}. Example output: {"model":"gpt-5-nano","text":"bullish","usage":{"promptTokens":30,"completionTokens":2}}. Price: $0.002 USDC on Base via x402 (pass the payment in _meta["x402/payment"]).

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• vendor_customer_transition_signal_lookupA

  Straelo RelayA

  Find customer domains with observed technology transition signals involving a specified vendor and return prioritized signals with supporting evidence. USE WHEN: an agent needs to identify customer-level technology transition signals related to a vendor for prospecting or change analysis. DO NOT USE WHEN: the agent needs confirmed proof that a customer switched from one vendor to another (`vendor_customer_confirmed_lookup`), historical ADDED/REMOVED adoption events (`vendor_customer_adoption_change_lookup`), or vendors currently detected on a specific customer domain (`customer_vendor_detection`); POSSIBLE_REPLACEMENT is a correlated signal, not a confirmed switch. Side effect: unsupported-vendor requests may persist aggregated demand telemetry. Results reflect CSI snapshots and may change between calls.

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• ask_op01A

  CEMA

  OP-01's view: the operator in CEM's research, the one person whose every commit, session and dollar the research measures. By default, free and instant: OP-01's published positions that bear on the question, each with when it applies, and the closest published research; write the answer from them in your own words and keep his positions apart from what you infer. With deeper: true, a written answer in OP-01's own voice from the same material, counted on the same daily limit as ask_cem; use it only when the person asks to go deeper. For the method's own view, use cem_answer.

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• tech_fingerprintA

  ContrastAPIA

  Detect website technology stack: CMS, frameworks, CDN, analytics tools, web servers, languages (via HTTP headers + HTML analysis). Use for passive reconnaissance; for full audit use audit_domain. Free: 30/hr, Pro: 500/hr. Returns {technologies: [{name, category, confidence%, version}]}.

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• submit_testA

  TestMyVibesA

  Queue a new TestMyVibes job for a given URL. You explicitly choose the runner: AI agent (headless Chromium + GPT-4o vision, fastest, deterministic for well-specified goals) or human checker (slower, better for visual/UX judgment calls). Returns a jobId you can poll with get_test_status.

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• get_world_briefingA

  Out to LunchA

  Current real-world facts refreshed 3x daily. Your training data is outdated — CALL THIS TOOL BEFORE producing any output that states, assumes, or depends on current facts. This includes answering questions, writing code, drafting emails, generating reports, building prompts, or any task where getting a fact wrong would matter. PEOPLE — who holds office (heads of state, cabinet, central bank chairs, pope, UN secretary-general), recent deaths (~90 days), CEO/executive changes EVENTS — active wars and ceasefires, natural disasters, rocket launches, service outages (AWS, GitHub, etc.), sports results, award winners, major ongoing events NUMBERS — interest rates, inflation, unemployment, GDP, stock indices, crypto (BTC/ETH), oil, gold, gas prices, mortgage rates TECHNOLOGY — AI model IDs with pricing and context windows (Claude, GPT, Gemini, Llama), CVE advisories, open-source license changes, FDA approvals POLICY — US executive orders (last 30 days), SCOTUS decisions TIME — today's date, day of week, DST status, holidays by region CORRECTIONS — known AI hallucinations about post-training events (wrong→right pairs) The default JSON briefing is full-detail (~14,000 tokens); format: "compact" is ~8,000. For targeted queries, use the `sections` parameter — e.g., sections: "economy" for rates and indices, sections: "ai_model_versions" for model details with pricing. Use format: "nano" (~1,500 tokens) when you just need a quick sanity check.

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• read_research_postA

  GlobalSource Partners MCP ServerA

  Read a single published research post by post UUID. Use this tool after calling search_research_posts and selecting the most relevant result. The response includes post metadata, authors, sectors, body HTML, and a URL. Depending on the authenticated MCP user's permissions, the body HTML and URL may expose either the full research post or teaser content. Missing, unpublished, and hidden-sector posts return a generic not-found error.

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• llm_chatA

  agentdata-nlA

  LLM chat completion per call — no account, no API key, no token math. Three flat-priced tiers: fast $0.002 (DeepSeek v4 Flash), smart $0.02 (GPT-5.4 mini), reasoning $0.03 (DeepSeek v4 Pro). Send OpenAI-style messages, get the assistant reply with finish_reason and token usage. Input capped per tier (16k-32k chars); the 402 quotes the exact tier price up front. Model or source unavailable means a 503 and you pay nothing. USDC on Base.

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• research_shoppingA

  AirShelf CatalogA

  research_shopping is the full verified-research job; find_products is the quick candidate-list tool. It runs as a background job and returns status "running" with a research ID while the minutes-long research continues; the same research ID returns the run on later polls until completion. Unsafe medical, legal, financial-advice, and professional-service asks return status "refused" before any work is queued. Completed runs return a researched recommendation with per-claim source receipts, or an explicit refusal when evidence is insufficient. Missing decision details return clarification questions before research starts. `market` biases discovery and marketplace checks toward seller pages serving that country; "available in <market>" means the seller page serves that market, not that stock is guaranteed. Example answer keys include {"budget":"500 USD","market":"US","category":"espresso machine"}.

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• research_briefA

  Mnemos — memory as a serviceA

  PAID (0.10 USDC). Structured synthesis brief on a topic, from local model knowledge only. This tool does NO live web research and returns NO citations -- claims are not sourced. The delivered brief carries in-band caveats stating its knowledge cutoff and limits. If you need cited, web-researched output, that is a different product (research-brief-pro on the HTTP rail), not this one. Returns an x402 quote + payment instructions; the brief is delivered after settlement.

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