"Research on Mimic GPT Technology" matching MCP tools:

• researchA

  SofyaA

  Perform comprehensive research on a topic. Decomposes your query into sub-queries, searches and reads multiple sources in parallel, then synthesizes a structured report with citations. Best for open-ended or comparative questions that need coverage from many angles. For simple factual lookups, use search instead (optionally with include_answer=true for cheap synthesis). Costs 50 credits. Returns: query, report (structured markdown with citations), sources (array of {title, url, fetched}), sub_queries (the decomposed queries), credits_used, credits_remaining, usage (token counts). Args: query: The research question or topic topic: "general" (default) or "news" (prioritize recent news articles) freshness: Filter by recency - "day", "week", "month", "year", or "YYYY-MM-DD:YYYY-MM-DD" max_sources: Maximum number of sources to use, 5-30 (default 20)

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• get_available_modelsA

  Gonka Second Opinion (multi-model)A

  List all AI models available on Gonka Network with live pricing. Models work as drop-in replacements for OpenAI and Anthropic — same SDK, same API calls. Use this when user asks which model to use or wants alternatives to GPT-4o / Claude. Returns: model IDs (use directly in openai.chat.completions.create), status, USD per 1M tokens. After this: call calculate_savings() to see annual savings with these models.

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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, 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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• omnarai_councilA

  omnarai-mcpA

  Summon a LIVE panel of frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) on one open question — verbatim answers, uncurated, plus the named tensions between them. Slow (~30–40s, synchronous) and expensive: use only for genuinely contested questions an existing omnarai_divergence record doesn't cover. Every run mints a new divergence record.

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• search_by_sentimentA

  Islam West Africa Collection (IWAC)A

  Filter articles by gpt-5-6-luna sentiment labels (accent/case-insensitive exact match). One model's reading, not a consensus — 4 other models scored the same articles and often disagree; get_sentiment_distribution with model:"all" shows by how much. `subjectivity` is much the weakest of the three scales, so treat a set selected on it as a lead to read rather than as a finding.

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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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• 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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• list_curated_flowsA

  OecdA

  List OECD dataflow refs we have pre-vetted, grouped by topic (gdp, labour, prices, finance, households, health, demographics, projections, tax, education, environment, technology). Pass the flow_ref to fetch_dataset. For everything else use search_dataflows or browse https://data-explorer.oecd.org.

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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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• enrichA

  x402-data-gatewayA

  Inspect a public company domain and return structured identity, technology, social, contact, DNS, email-infrastructure, and AI-readiness evidence. Use `schemaforge` instead for a paste-ready JSON-LD template and remediation diff, or `deep_audit` when both outputs are required together. Public data only; this tool makes no site changes.

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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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• generate_imageA

  switchA

  Generate one or more Switch images. Auto-routes to the right model based on subject (Nano Banana 2 default, GPT Image 2 for swimwear/beach, Switch Model/Ultra/Pro for sexier content, Nano Banana Pro for typography-heavy). 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. Pass an optional `style` (e.g. "wellness/warm_amber_tropical", "high_fashion_editorial/testino_glossy", "movie_scene/neon_noir_action") to apply a curated photographic stack from the apply_* skill tools.

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• search_researchA

  HeistaA

  Structured fact-check + numerical research via Perplexity Sonar Reasoning Pro (Gateway-routed). Returns synthesized answer text plus structured sources[] with direct URLs to primary sources. Use for: specific numerical claims with methodology context, fact-check against primary sources, effect sizes + confidence intervals, earnings transcripts / SEC filings / research papers. Per Phase 3.5 empirical A/B: 2-3× cheaper than sonar-pro with comparable or better quality on structured research. Real Meta IR press releases + earnings transcripts on Desk. 17 cites on Quant. NOT for: Reddit/X/community → use search_community. NOT for: broad topic landscapes → use search.

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• get_case_studyA

  Agrus.ai — Enterprise AI AgencyA

  Returns one or more Agrus case studies (NDA-protected; customer names are kept private, codenames + technology + outcomes are open). Filter by slug or vertical, or call with no args to list all. Use this for proof of prior work.

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• v3_search_eventsA

  V³ NewsA

  Search V³ News for tracked geopolitical events. Use when the user asks what is happening on a topic, in a country, or in a domain (e.g. security, energy, finance, technology). Returns a ranked list of events with why-it-matters, risk/impact/signal scores, and a v3.news link for each. Args: query: free-text topic (e.g. "Iran sanctions", "Taiwan"). Optional. country: ISO-3166 alpha-2 code to filter by (e.g. "US", "CN"). Optional. category: V³ category slug — one of geopolitics, security_risk, energy_resources, finance, markets, macroeconomics, public_finance, trade_supply, technology, science_biosecurity, environment_climate, business. Optional. limit: max results, 1-20 (default 10).

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• count_tokensA

  inktomd MCP ServerA

  Count the exact number of tokens in a text string for a specific AI model. Uses tiktoken for OpenAI models and estimates for others. Args: text: The text to count tokens for model: The AI model to count tokens for. Options: gpt-4o, gpt-4o-mini, gpt-4.1, claude-sonnet, claude-haiku, gemini-pro, gemini-flash, llama-4, deepseek-v3, mistral-large. Default: gpt-4o Returns: Token count information including count, context window, and fit status

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• deep_researchA

  netcafe-docsA

  Start an autonomous web research task. The agent plans sub-questions, searches the web, reads the sources and writes a report with citations — this is real research, not a single model call, and takes 2-5 minutes. Returns a job_id immediately; poll check_job to get the report. Use this when you need sourced, current information rather than what a model already knows. Powered by gpt-researcher (29k stars) hosted at AI NetCafé. Example — tools/call deep_research {"topic":"State of MCP adoption in 2026?"} → poll check_job

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• a9n9_research_startA

  a9n9 Deep ResearchA

  Start bounded paid research in a real remotely hosted Browserbase Chromium session with JavaScript rendering, DOM interaction, scrolling, and dynamic-content extraction, followed by gpt-5.6-luna synthesis. targetUrl is optional; without it the quoted plan of public start URLs derived from the objective is executed. Call a9n9_research_quote first when price visibility is needed. The service does not bypass CAPTCHAs, logins, paywalls, access controls, or site terms.

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• anansi_price_currentA

  anansi-dataA

  FREE. Current per-token prices for LLMs as recorded at our last poll: usd_per_prompt_token and usd_per_completion_token (USD per single token, not per million), context window in tokens, and the observation timestamp (unix seconds). Use this to check what a model costs right now, or to compare a handful of models before routing. Do NOT use it to see how a price MOVED — it returns only the latest observation; use anansi_price_changes_recent (free) or anansi_price_history (paid). Input: model_id is a substring match, so 'gpt-5' matches every gpt-5 variant; resolve exact IDs with anansi_search.

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• get_model_historyA

  inferenceindexer-mcpA

  Get HISTORICAL price data / trends for one model. This is InferenceIndexer's differentiator: aggregators like OpenRouter expose only current price; this returns the price over time (input, output, blended $/M), enabling trend analysis. Args: model_id: Canonical model id, e.g. 'openai/gpt-5.6'. days: History window in days (1-365, default 30; plan-dependent). Returns: historical price series for the model.

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