"Research on Mimic GPT Technology" matching MCP tools:

• research_niche

  KDP Niche Research

  Get detailed KDP niche intelligence for a specific keyword. Returns demand score, competition score, Amazon BSR range, estimated monthly revenue, review threshold, average book pricing, and data freshness for the given Kindle publishing niche. Pricing tiers (x402 USDC on Base network): - $0.03 per query for cached/pre-seeded keywords - $0.10 per query for live on-demand research (new keywords) Use the free `list_niches` tool first to see available keywords. Payment options: 1. Set the KDP_X_PAYMENT environment variable on the server for auto-pay. 2. Pass a valid x402 payment header via the x_payment argument. 3. If neither is set, the tool returns structured 402 payment instructions that an x402-capable agent can use to construct and retry payment. Args: keyword: The KDP niche keyword to research (e.g. "romance novels", "keto cookbook") x_payment: Optional base64-encoded x402 payment header. Takes precedence over the KDP_X_PAYMENT environment variable.

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• register_agent

  Arcology Knowledge Node

  Register as an agent to get an API key for authenticated submissions. Registration is open — no approval required. Returns an API key that authenticates your proposals and tracks your contribution history. IMPORTANT: Save the returned api_key immediately. It is shown only once and cannot be retrieved again. Args: agent_name: A name identifying this agent instance (2-100 chars) model: The model ID (e.g., "claude-opus-4-6", "gpt-4o")

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• web__local_search

  DialogBrain

  Multi-source web research with citations. Returns a synthesized answer with numbered [^1] markers and a citations array of {url, title, snippet, index}. Use for evidence-backed synthesis (competitive analysis, regulatory summary, whitepaper section). For quick fact lookups use web.search instead.

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• browse_by_tag

  AI Agents Overflow

  Browse the knowledge base by technology tag at the START of a task. Call this when beginning work with a specific technology to discover what verified knowledge already exists — before you hit problems. Examples of useful tags: 'pytorch', 'cuda', 'fastapi', 'docker', 'ros2', 'numpy', 'jetson', 'arm64', 'postgresql', 'redis', 'kubernetes', 'react'. Returns a list of questions (title + tags + score) for the given tag, ordered by community score. Call `get_answers` on relevant results.

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• audit_stack

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.

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• check_attestations

  xProof

  Check domain-specific attestations for an AI agent wallet on xproof. Returns active attestations issued by third-party certifying bodies (healthcare, finance, legal, security, research). Each active attestation adds +50 to the agent's trust score (max +150 from 3 attestations). Use this to verify an agent's credentials before delegating a sensitive task.

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• searchJobs

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  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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• search_solutions

  Civis

  Semantic search across the Civis knowledge base of agent build logs. Returns the most relevant solutions for a given problem or query. Use the get_solution tool to retrieve the full solution text for a specific result. Tip: include specific technology names in your query for better results.

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• createDeepResearch

  Parallel Task MCP

  Creates a Deep Research task for comprehensive, single-topic research with citations. USE THIS for analyst-grade reports, NOT for batch data enrichment. Use Parallel Search MCP for quick lookups. After calling, share the URL with the user and STOP. Do not poll or check results unless otherwise instructed. Multi-turn research: The response includes an interaction_id. To ask follow-up questions that build on prior research, pass that interaction_id as previous_interaction_id in a new call. The follow-up run inherits accumulated context, so queries like "How does this compare to X?" work without restating the original topic. Note: the first run must be completed before the follow-up can use its context.

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• generate_image

  Frenchie

  Generate a single image from a text prompt through Frenchie (gpt-image-2). 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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• explore

  Civis

  Proactive discovery: "Here is my stack, what should I know?" Returns build logs relevant to your technology stack, ranked by stack overlap, pull count, and recency. Unlike search_solutions, this does not require a specific query; it finds relevant knowledge based on the technologies you work with. Use the focus parameter to narrow results to a specific category. Use the exclude parameter to skip build logs you have already seen.

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• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

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• get_broadband_by_location

  FCC Broadband Map

  Get broadband providers and availability at a specific lat/lon location. Returns a list of broadband providers serving the location with their advertised download/upload speeds and technology types. Includes BEAD classification (unserved/underserved/served) based on max available speeds. NOTE: The FCC Broadband Map API has bot protection and may reject requests. If you get an error, the API endpoint may have changed. The FCC updates this API frequently without notice. Args: latitude: Location latitude (e.g. 38.8977 for Washington DC). longitude: Location longitude (e.g. -77.0365 for Washington DC). technology_code: Filter by technology (0=All, 10=Copper, 40=Cable, 50=Fiber, 60=Satellite, 70=Fixed Wireless). speed_download: Minimum download speed in Mbps (default 25). speed_upload: Minimum upload speed in Mbps (default 3). as_of_date: BDC filing date in YYYY-MM-DD format (default 2024-06-30).

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• fix_site

  AEO Scanner

  Generate complete fix code for all AI visibility issues across AEO, GEO, and Agent Readiness. Returns working code you can apply directly — schema generation, robots.txt, sitemap, llms.txt, meta tags, structured data, citation signals, entity markup. Also returns two-tier score projections: quick wins (critical + high fixes only) and full implementation ceiling (all fixes). Content recommendations include research citations. Run scan_site first to see which issues exist. Pay per call ($5.00) via x402 — USDC on Base or Solana. On payment_required, the response includes the full x402 payload with payTo/amount/asset.

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• reset_session

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Kill a broken research session and start fresh. Use this when a session gets stuck, produces errors, or enters a bad state. Free — no credits consumed. After resetting, call research without a session_id to start a new clean session.

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• zk_list_circuits

  Hive Zk Attestation

  Enumerate supported circuits and verification key fingerprints. Primary: Varuna over BLS12-377 (Aleo snarkVM-compatible). Research-stage: Groth16, Plonk. Future: Risc0, Plonky2. Free. Read-only.

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• list_chats

  Peec AI MCP Server

  List chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: only chats from the given engine channel (openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) If both model_id and model_channel_id are provided, model_channel_id takes precedence and model_id is ignored. Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount, totalCount}. rowCount is the rows in this page; totalCount is the total matching records ignoring limit/offset. Columns: id, prompt_id, model_id, model_channel_id, date.

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• GetAnalystRatings

  Fintel Discovery — Financial Intelligence for AI Agents

  Fetch analyst buy/sell/hold consensus ratings, current price targets (low, high, mean, median), and the full history of analyst upgrades and downgrades with firm name, fromGrade, toGrade, and action. Use this tool when: - You want to know the current Wall Street consensus on a stock - You need analyst price target range (upside/downside to target) - You are tracking rating changes from major research firms Source: Yahoo Finance via yfinance. No API key required.

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• count_tokens_multi

  Toolora MCP Server

  Estimate token count + USD cost for a text across every major LLM (GPT-4o, GPT-4o-mini, o1, o1-mini, Claude 3.5 Sonnet/Haiku, Claude 3 Opus, Gemini 1.5 Pro/Flash, Llama 3 70B/8B) in one call. Returns per-model: estimated tokens, context-window fit %, input cost, and roundtrip cost (input+output). Also returns the cheapest and costliest model that fits. Use this before sending a long context to decide which model to route to. One call replaces 11 separate tokenizer lookups.

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• list_stack_tags

  Civis

  Returns the complete list of valid, canonical technology tags that Civis recognizes. Use this to find the correct tag names before calling search_solutions or explore. Tags are organized by category (ai, framework, database, language, etc.). Common aliases are auto-resolved (e.g. "nextjs" resolves to "Next.js"), but using canonical names is recommended.

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