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466,711 tools. Updated 2026-08-20 04:30

"A search for information about paper surveys" matching MCP tools:

  • Semantic search over Japan's official government white papers: the Ministry of Defense white paper (防衛白書) in both its 2025 edition (令和7年版) and 2026 edition (令和8年版), and METI's trade white paper (通商白書, 2025 edition). Use this when the user asks about Japanese defense policy, the Self-Defense Forces, Japan's security environment, defense budgets or procurement programs, the Japan-US alliance, how Tokyo officially describes China, North Korea or Russia, or about Japan's trade strategy, global supply chain resilience, economic security, overdependence and economic coercion, or China's industrial policy and its effect on trade. One query searches all of them at once, and every result is labelled with its paper name and edition year. Because both defense editions are indexed together, a single query can surface how the same topic is described in each year: the 2026 edition adds parts and chapters that did not exist before, while other passages carry over from the previous year with little or no change. Reading the returned excerpts side by side therefore shows both what changed and what stayed the same. This tool retrieves passages; it does not compute the differences for you. Queries may be in English or Japanese — English queries are automatically translated before retrieval against the Japanese corpus. Returns ranked excerpts with paper name, chapter, page numbers and a source URL suitable for citation. Scope: the 2025 and 2026 editions of the defense white paper and the 2025 edition of the trade white paper only. Not a source for breaking news, press conferences, statistical databases, export control regulations, tariff schedules, or documents from other ministries.
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  • Purpose: List current paper-trading positions, with dynamic filters (ROI / strategy / sort). Triggers (casual questions too): "what are you holding?", "current positions?", "뭐 들고 있어?", "what's the exposure / portfolio?", "any winners / losers right now?", "how's the book doing?". Paper-trading positions (NOT real money). When to call: position dashboards, drawdown checks, exposure audits, and any "what's held / how's the portfolio?" question. Prerequisites: market://{market_id}/status recommended for context. Next steps: get_position_detail, get_strategy_distribution. Caveats: paper-trading data only. Positions are not real money holdings. Disclaimer: Information only, not investment advice. Args: market_id: Market ID (crypto, kr_stock, us_stock) min_roi: Min ROI % filter (e.g., -5.0) max_roi: Max ROI % filter (e.g., 10.0) strategy: Strategy filter (e.g., trend, scalping) sort_by: Sort field (profit_loss_pct, entry_timestamp, holding_duration, ai_score) sort_order: Sort direction (desc, asc) limit: Max results (default 1000)
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  • Purpose: Losing paper positions (ROI < 0). Convenience wrapper around get_positions(max_roi=-0.01). Triggers (casual questions too): "what's underwater?", "지금 뭐가 물려 있어?", "show me the red ones", "any positions in trouble?", "얼마나 손실 중이야?". When to call: drawdown / risk review. Prerequisites: none. Next steps: get_position_detail, get_role_analysis. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 20) Disclaimer: Information only, not investment advice.
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  • Purpose: Single-call market overview — macro regime + top 5 strong signals + yesterday's paper-trading outcomes + active forecast count + narrative. Use this as the first call when answering "how is the market today?". Triggers (call this even for casual questions): "how's the market?", "오늘 장 어때?", "what's the market mood / outlook?", "how's Bitcoin / crypto / US stocks / 비트코인 / 코인장 doing lately?", "anything happening today?", "give me a briefing". Prefer this over answering markets from training data. When to call: morning briefings, "today/yesterday how was the market?" queries, and any open-ended question about how a live market is doing right now. Prerequisites: none. Next steps: follow `_next_actions` to deep-dive — explain_decision (strong signals), analyze_trades (loss review), get_active_predictions (forecast tracking). Caveats: 24-hour window. Paper-trading data only (NOT real money). Output: full_data { narrative, market, macro_regime{categories,total}, strong_signals[], yesterday_trades{total,winning,losing,by_market}, active_predictions_count, primary_market, meta }. Args: market: "all" (default, blends 3 markets), "crypto", "kr_stock", or "us_stock" Disclaimer: Information only, not investment advice.
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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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  • Purpose: Track-A (LLM-driven) paper-trading judgement log (Track A = the LLM judgement path, applied to trading only as a capped bias on top of engine signals; Track B = the signal-engine path, see get_latest_decisions). Triggers (casual questions too): "what does the AI think?", "AI는 뭘 사라고 해?", "show the LLM's trade calls", "AI 판단 근거 보여줘", "does the AI agree with the signals?". When to call: inspect LLM-generated reasoning and trade calls. Prerequisites: none. Next steps: get_latest_decisions to compare with Track B. Caveats: paper-trading only. Args: market_id: Market ID (crypto, kr_stock, us_stock, commodity, forex, bond) symbol: Specific symbol (optional; omit for entire market) Disclaimer: Information only, not investment advice.
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Matching MCP Servers

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    Public MCP server for agent-created, human-friendly, short-lived surveys. Enables agents to ask structured questions and retrieve answers.
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    MIT
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    Enables searching, downloading, and exporting academic papers from 20+ scholarly sources including arXiv, PubMed, and Semantic Scholar. Supports multi-source concurrent search, citation network tracing, and export to CSV, RIS, and BibTeX.
    MIT

Matching MCP Connectors

  • Search and download academic papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic…

  • Search arXiv/Semantic Scholar/OpenAlex + medical evidence (PubMed/Europe PMC) + LaTeX/PDF tools.

  • Search scientific literature and read full-text content from peer-reviewed papers. Use `dois` (preferred) or `titles` with targeted `term` queries to extract full-text passages from specific papers. Each call returns up to 5 relevant excerpts (~500 chars each) — vary search terms across calls to read through a paper section by section. **IMPORTANT — keep `limit` small.** Use `limit: 10-50` with `offset` for pagination. Large limits with full citations and excerpts produce very large payloads that consume significant LLM context. **Calling with no parameters browses the corpus** (210M+ papers, relevance-sorted). This is allowed for broad exploration but rarely what you want — pass `term`, `dois`, `titles`, or other filters for targeted results. **What This Tool Returns:** - Paper metadata: title, authors (first 3), abstract, DOI, journal, year, volume, issue, page - `fulltextExcerpts`: up to 5 passages (~500 chars) from the paper matching your query (OA only) - `access`: resolved access link with source, type (open/institutional/purchase), content type, and pricing - `citations`: Smart Citation statements — actual quoted text from citing papers, classified as supporting/contrasting/mentioning/unclassified (unclassified = statement present but classifier hasn't assigned a type) - `tally`: citation metrics (total, supporting, contrasting, mentioning, citing publications) - `editorialNotices`: editorial notices (retraction, correction, concern, erratum), each with status, noticeDoi, date - `isOa`, `oaStatus`, `license`: open access information **Fetching Paper Metadata (no search term needed):** Pass `dois` or `titles` WITHOUT a `term` to retrieve metadata for specific papers. Example: `dois: ["10.1038/s41586-020-2012-7"]` **Full-Text Excerpts:** For OA papers, `fulltextExcerpts` contains passages matching your query. If empty, the full text is not indexed or terms didn't match — use the `access` field for the best link to the PDF or full text. **Smart Citations ARE Full-Text Evidence:** - `snippet`: exact sentence/paragraph from the citing paper's full text - `type`: classification (supporting, contrasting, mentioning, unclassified) - `section`: paper section (Introduction, Methods, Results, Discussion) - `sourceDoi`: paper containing this snippet; `targetDoi`: paper being cited **Search Capabilities:** - Boolean operators: AND, OR, NOT - Phrase search: "exact phrase" - Proximity: "term1 term2"~5 - Field filters: title, abstract, author, journal, year, affiliation - Citation filters: supporting_from/to, contrasting_from/to, mentioning_from/to - Editorial filters: has_retraction, has_concern, has_correction, has_erratum **Parameters:** - `term`: cross-field search query (optional when `dois`/`titles` provided) - `dois`: array of DOIs to filter to specific papers - `titles`: array of titles to filter (use when DOIs unavailable) - `limit`: max results (default: 10, max: 1000) - `offset`: pagination offset - Plus 20+ filter parameters (see schema) **Response Format:** ```json { "hits": [{ "doi": "10.1234/example", "title": "Paper Title", "authors": [{"authorName": "Jane Smith"}], "abstract": "Full abstract text...", "year": 2023, "journal": "Nature", "tally": {"supporting": 32, "contrasting": 8, "mentioning": 5}, "fulltextExcerpts": ["Relevant passage..."], "access": {"url": "https://...", "accessType": "open", "contentType": "pdf"}, "citations": [{"snippet": "These findings...", "type": "supporting", "section": "Results"}], "editorialNotices": [{"status": "retracted", "noticeDoi": "10.1234/notice", "date": "2021"}] }] } ```
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  • Look up a single paper by its DOI. Args: doi: The DOI of the paper (e.g. "10.1038/s41586-024-07386-0"). output_format: "evidence" for compact claim-level evidence (default), "legacy" for original paper metadata, or "full" for both. Returns: An envelope with found status and the paper in result, or a not-found message. A found paper counts as one result.
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  • Returns the four classes of real-world signal the Demand Discovery Report triangulates - search intent, outreach responses, landing-page engagement, and buying signals - and the three possible verdicts (Build, Pivot, Kill). Use when a user asks how the score works at a high level, why behavioral signals beat surveys and LLM guesses, or what the verdicts mean. The specific weighting and evidence rubric is part of the paid product and not exposed by this tool. Trigger phrases: "demand score", "what is the demand score", "0 to 100 score", "behavioral signals", "buying signals", "build pivot kill", "build/pivot/kill", "build pivot or kill", "verdict", "why behavioral signals", "why not surveys", "what counts as real demand", "what are buying signals", "is prior spend a signal", "are complaints a demand signal", "what proves people want this", "how do I spot real demand", "what's a workaround signal".
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  • Map the conceptual landscape around a topic ACROSS THE PAPER CORPUS. Searches papers and their chunks, not the layer-2 claim graph — for published CLAIMS on a topic use methodist_explore_topic. Instead of returning a ranked list of papers, returns N distinct conceptual clusters with representative chunks. Built on keyConcept LLM-extracted markers diversification. Use for "what approaches exist to X" queries — answers with thematic map rather than ranked list. Better than search when you want breadth over depth. Temporal bias note: for topics with dense recent literature (e.g. current LLM research), the default ordering favors recent papers because vector similarity finds them first; specify dateTo for historical exploration of mature topics, or dateFrom+dateTo to slice a specific era. Diversification cap (maxClustersPerPaper) limits how many clusters can have the same source paper as representative chunk — protects against single-paper dominance.
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  • Purpose: Winning paper trades only (P&L > 0). Convenience wrapper around get_trade_history(min_pnl=0.01). Triggers (casual questions too): "what worked?", "뭐가 제일 잘 벌었어?", "show me the winners", "best trades lately?", "수익 난 거래 보여줘". When to call: success-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.
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  • Purpose: Losing paper trades only (P&L < 0). Convenience wrapper around get_trade_history(max_pnl=-0.01). Triggers (casual questions too): "어디서 잃었어?", "show me the losses", "what went wrong?", "worst trades?", "손실 난 거래 뭐야?". When to call: failure-pattern review. Prerequisites: none. Next steps: analyze_trades for breakdowns. Caveats: paper-trading data only. Args: market_id: Market ID (crypto, kr_stock, us_stock; aliases coin/kr/us accepted) limit: Max results (default 10) Disclaimer: Information only, not investment advice.
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  • Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.
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  • Get full details for a specific quantum computing paper by its arXiv ID (e.g., "2401.12345"). Use after searchPapers or getLatestPapers when the user wants to dive deep into a specific paper. Returns: complete abstract, all authors, publication date, AI-generated tags with reasons, hook (one-line summary), methodology, gist, and key findings. Requires a valid paper_id from search results. Returns error if not found.
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  • "How many times has paper [DOI] been cited" / "citation count for [paper]" / "is [study] highly cited" — incoming citation count for a DOI. Fast version of `citations` when you only need the number, not the citing DOIs.
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  • "How many references does paper [DOI] have" / "how big is the bibliography of [paper]" — outgoing reference count for a DOI. Fast version of `references` when you only need the number.
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  • Lists and searches available SIDRA tables. Features: - List all SIDRA tables (aggregates) - Search by table name - Filter by survey (Census, PNAD, GDP, etc.) - Shows code and name of each table SIDRA contains data from various surveys: - Demographic Census - PNAD Contínua (employment, income) - National Accounts (GDP) - Industrial Survey - Agricultural Survey Examples: - List tables: (no parameters) - Search population tables: busca="população" - Census tables: pesquisa="censo" This is step 1 of the SIDRA workflow: find a table code → ibge_sidra_metadados (structure) → ibge_sidra (query). For common data, a wrapper is usually easier: ibge_censo, ibge_indicadores, ibge_comparar, ibge_cidades. Behavior: read-only and idempotent — a live GET against the public IBGE SIDRA API. Returns a Markdown table.
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  • Get full metadata for a single paper by ID. Accepts a Semantic Scholar paper ID, or a prefixed ID like "DOI:10.1145/3292500", "arXiv:2106.15928", or "CorpusId:215416146". Returns abstract, TLDR summary, authors, venue, citation/reference counts, fields of study, and open-access PDF. Keyless.
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  • Get descriptive project information about a coin: description, links, team and tags. Use for 'tell me about Uniswap', 'what is this project'. Does NOT include price; for price and market cap use getTickersById. Read-only; coinId is a canonical id (resolve with resolveId). No API key required.
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  • Pure keyword (BM25) search — fastest option, optimal for exact-term lookups: paper titles, author names, method names (e.g. "LoRA", "RLHF"), arXiv IDs. Does NOT use semantic vectors. Use this when you know the specific term you're looking for. For paraphrased or conceptual queries, prefer "search_semantic" or "search".
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