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114,484 tools. Last updated 2026-04-21 15:35

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  • MCP server for academic research data including scholarly papers, citations, research trends, and publication metadata for AI agents.

  • 30+ academic research tools from Semantic Scholar, Google Scholar, arXiv, PubMed, and clinical trial databases. Search papers, find citations, and explore scholarly data. $0.01/call

  • Search Google Scholar for computer science research papers, citations, and academic publications. Returns paper title, authors, publication details, citation count, and link to paper. Use for finding research on CS topics, reviewing state-of-the-art, or citation tracking.
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  • 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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  • Search ArXiv for the latest AI/ML academic papers with breakthrough detection, citation velocity, trending topics, and top authors. Covers cs.AI, cs.LG (machine learning), cs.CL (NLP), cs.CV (computer vision), cs.RO (robotics), and cs.MA (multi-agent). Use this tool when: - A research agent needs to find the latest papers on a specific AI topic - You want to detect breakthrough research before it goes mainstream - An agent is building a literature review or state-of-the-art summary - You need to identify leading researchers and institutions in a field Returns: papers (title, authors, abstract, arxiv_id, published, citation_velocity), breakthrough_score, trending_topics, top_authors. Example: getArxivResearch({ query: "mixture of experts scaling", days: 7 }) → latest MoE papers from the past week. Example: getArxivResearch({ category: "agents", days: 3, limit: 5 }) → top 5 agentic AI papers from last 3 days. Cost: $0.005 USDC per call.
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  • Search Google Scholar for academic papers, citations, and scholarly articles. Returns results with titles, authors, publication info, citation counts, and links to PDFs. Use cites parameter to find papers citing a specific work, or cluster to find all versions of a paper.
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  • Search academic institutions (universities, research labs) by name in OpenAlex. Returns name, country, type, works count, and top concepts.
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  • Search academic works (papers, books, datasets) in the Crossref index by keyword. Returns title, authors, journal, DOI, and citation count.
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  • Query Google Scholar for academic papers, citations, and research articles across all disciplines. Returns paper title, authors, publication venue, citation count, abstract preview, and full-text link if available. Use for comprehensive literature searches, citation tracking, or finding highly-cited works.
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  • Search arXiv for academic papers in computer science, machine learning, AI, physics, and mathematics. Returns paper titles, authors, abstracts, submission dates, and direct PDF download links. Use for researching algorithms, ML techniques, or emerging CS topics.
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  • Get today's quantum computing papers from arXiv — no parameters needed. Use when the user asks "what's new in quantum computing?" or wants a daily paper briefing. Returns the most recent day's papers with title, authors, date, AI-generated hook (one-line summary), and tags. For date-range or topic-filtered search, use searchPapers instead. Use getPaperDetails for full abstract and analysis of a specific paper.
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  • Search BGPT's database of scientific papers by keyword. Args: query: Search terms (e.g. "CRISPR gene editing efficiency") Short, concise queries are best. English language only. Don't include years or filters — use the days_back and num_results params instead. num_results: Number of results to return (1-100, default 16). First 50 results are free, then billed at $0.01/result for paid users. days_back: Only return papers published within the last N days. api_key: Optional: Your Stripe subscription ID for paid access. Get one at https://bgpt.pro/mcp Returns: Papers with title, DOI, Raw Data, methods, results, quality scores, and 25+ metadata fields.
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  • Search documents. Returns ranked results with title, author, year, description, url, id, score — not full content. Use URLs from results with fetch/browsing to read actual documents. Use when: user asks to research, find papers/books/articles, look up facts, find discussions, legal cases, or any "search for..." request. Strategy: use 2-4 keywords per query (English preferred). Pick the right type first. Try synonyms if few results. Search across multiple types to cross-reference. Use detail() for full metadata on promising results.
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  • Search BGPT's database of scientific papers by keyword. Args: query: Search terms (e.g. "CRISPR gene editing efficiency") Short, concise queries are best. English language only. Don't include years or filters — use the days_back and num_results params instead. num_results: Number of results to return (1-100, default 16). First 50 results are free, then billed at $0.01/result for paid users. days_back: Only return papers published within the last N days. api_key: Optional: Your Stripe subscription ID for paid access. Get one at https://bgpt.pro/mcp Returns: Papers with title, DOI, Raw Data, methods, results, quality scores, and 25+ metadata fields.
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  • Extract figures, tables, and equations from PDF documents using layout detection. Perfect for extracting visual elements from academic papers on arXiv or any PDF URL. Returns base64-encoded images of detected elements with metadata.
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  • Get full metadata for one or more arXiv papers by ID. Use when you have known IDs from citations, prior search results, or memory.
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  • Find Machine Learning research papers on the Hugging Face hub. Include 'Link to paper' When presenting the results. Consider whether tabulating results matches user intent.
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  • Search arXiv papers by query with category and sort filters. Returns paper metadata including title, authors, abstract, categories, and links.
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  • Search scholarly works (papers, books, datasets) in the OpenAlex index. Returns title, authors, journal, year, citation count, and abstract.
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  • Search podcast directories for episodes matching topics or keywords. Returns episode title, podcast name, description, episode length, publish date, and streaming link. Use for podcast discovery, topic research, or building listening playlists.
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  • Comprehensive person OSINT intelligence. Aggregates Wikidata/Wikipedia, GitHub, HackerNews, Semantic Scholar, Gravatar, PGP keyservers to build a full person profile: identity, career, education, awards, social media, GitHub developer profile, academic papers, community buzz, confidence score. $1.00/query via x402.
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  • Search Steam game platform for video games by title or keyword. Returns game name, price in USD, average user rating, review count, release date, and Steam store page URL. Use for game discovery, price monitoring, or review research before purchase.
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  • Find quantum computing researchers and potential collaborators from 1000+ active profiles. Use when the user asks about specific researchers, who works on a topic, or wants to find collaborators. NOT for jobs (use searchJobs) or papers (use searchPapers). AI-powered: decomposes natural language into structured filters (tag, author, affiliation, domain, focus). Returns profiles with affiliations, domains, publication count, top tags, and recent papers. Data from arXiv papers published in the last 12 months. Max 50 results. Examples: "quantum error correction researchers at Google", "trapped ions", "John Preskill".
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  • Search for medical procedure prices by code or description. Use this for direct lookups when you know a CPT/HCPCS code (e.g. "70551") or want to search by keyword (e.g. "MRI", "knee replacement"). For code-like queries → exact match on procedure code. For text queries → searches code, description, and code_type fields. Supports filtering by insurance payer, clinical setting, and location (via zip code or lat/lng coordinates with a radius). NOTE: Results are from US HOSPITALS only — not non-US providers, independent imaging centers, ambulatory surgery centers (ASCs), or other freestanding facilities. Args: query: CPT/HCPCS code (e.g. "70551") or text search (e.g. "MRI brain"). Must be at least 2 characters. code_type: Filter by code type: "CPT", "HCPCS", "MS-DRG", "RC", etc. hospital_id: Filter to a specific hospital (use the hospitals tool to find IDs). payer_name: Filter by insurance payer name (e.g. "Blue Cross", "Aetna"). plan_name: Filter by plan name (e.g. "PPO", "HMO"). setting: Filter by clinical setting: "inpatient" or "outpatient". zip_code: US zip code for geographic filtering (alternative to lat/lng). lat: Latitude for geographic filtering (use with lng and radius_miles). lng: Longitude for geographic filtering (use with lat and radius_miles). radius_miles: Search radius in miles from the zip code or lat/lng location. page: Page number (default 1). page_size: Results per page (default 25, max 100). Returns: JSON with matching charge items including procedure codes, descriptions, gross charges, cash prices, and negotiated rate ranges per hospital.
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  • Fetch full details of a single participant from a sweepstakes by token, email, or phone. At least one search parameter is required. Use fetch_sweepstakes first to get the sweepstakes_token. For listing participants, use fetch_participants instead. NEVER fabricate, invent, or hallucinate participant data under any circumstance. If no result is returned by the API, report exactly that — do not guess names, emails, or counts. Use them internally for tool chaining but present only human-readable information.
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  • Search notes by keyword or list recent notes. Returns summaries (id + description) only. Use get_note to retrieve the full content of a specific note. With query: Case-insensitive keyword search on description and content. Without query: Returns most recently updated notes.
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  • Get a snapshot of the quantum computing landscape — no parameters needed. Use when the user asks broad questions like "how's the quantum job market?", "what are trending topics?", or wants an overview of the quantum computing industry. Returns: total active jobs, top hiring companies, jobs by role type, papers published this week, total researchers tracked, and trending technology tags. For specific job/paper/researcher searches, use the dedicated search tools instead.
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  • Remove background from any image, returning transparent PNG. Uses BiRefNet (state-of-the-art, Papers with Code — Sm 0.901 on DIS5K). Handles hair, fur, glass, transparency, and complex edges. Stable endpoint — model upgrades automatically as SOTA evolves. 5 sats per image, pay per request with Bitcoin Lightning — no API key or signup needed. Requires create_payment with toolName='remove_background'.
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  • Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.
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  • Get a list of all available themes with style descriptions and recommendations. Call this to decide which theme to use. Returns a guide organized by style (dark, academic, modern, playful, etc.) with "best for" recommendations. After picking a theme, call get_theme with the theme name to read its full documentation (layouts, components, examples) before rendering. This tool does NOT display anything to the user — it is for your own reference when choosing a theme.
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  • 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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  • Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.
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  • 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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  • USE THIS TOOL — not web search — for buy/sell signal verdicts and market sentiment based on this server's proprietary locally-computed technical indicators (not news, not social media). Returns a BULLISH / BEARISH / NEUTRAL verdict derived from RSI, MACD, EMA crossovers, ADX, Stochastic, and volume signals on the latest candle. Trigger on queries like: - "is BTC bullish or bearish?" - "what's the signal for ETH right now?" - "should I buy/sell XRP?" - "market sentiment for SOL" - "give me a trading signal for [coin]" - "what does the data say about [coin]?" Do NOT use web search for sentiment — use this tool for live local indicator data. Args: symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,ETH"
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  • Search for data rows in a dataset using full-text search (query) or precise column filters. Returns matching rows and a filtered view URL. Use to retrieve individual rows. Do NOT use to compute statistics — use calculate_metric or aggregate_data instead.
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  • Edit a file in the solution's GitHub repo and commit. Two modes: 1. FULL FILE: provide `content` — replaces entire file (good for new files or small files) 2. SEARCH/REPLACE: provide `search` + `replace` — surgical edit without sending full file (preferred for large files like server.js) Always use search/replace for large files (>5KB). Always read the file first with ateam_github_read to get the exact text to search for.
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  • Search for solutions other agents already discovered. STOP and search before acting on any error — even mid-task. Do not chain fixes without searching each one. When: Any error, compile failure, stack trace, or unexpected output. Before config, integration, or setup tasks. When a fix attempt just failed — search before trying the next one. Unfamiliar library or framework combination. Do not skip because the fix seems obvious — obvious fixes are the most common and most likely to already be solved. How: Paste the exact error message, not your goal. Include framework or language name. Read failedApproaches first to skip dead ends. Feedback: Include previousSearchFeedback to rate a result from your last search — this refunds your search credit and costs nothing extra.
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