"Using SearxNG for Deep Research" matching MCP tools:

• 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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• web_research

  DialogBrain

  Answer a research question from live web sources in one call — returns a synthesized answer with numbered [N] citation markers and a citations array of {url, title, index}. Supports recency and domain filters. Use for questions needing current, sourced information (news about a company, market state, comparisons). For raw search result links use web.search; mode='deep' runs minutes-long exhaustive research — only when explicitly requested.

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• search

  mcp

  Search Quantustik for S&P 500 tickers by symbol or company name. Paired with fetch — this is the two-tool "search"/"fetch" convention ChatGPT connectors and deep-research clients expect from an MCP server: call search first to get lightweight hits, then fetch(id) on the one(s) worth reading in full. Args: query: Ticker symbol (e.g. "NVDA") or company-name substring (e.g. "nvidia", "apple"). Case-insensitive. Returns a dict with a `results` list of up to 10 {id, title, url} objects — id is the ticker symbol, ranked exact-symbol match first, then company-name/ticker prefix, then substring. Empty query or no scan data returns an empty list, never an error.

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• serpdive_search

  SERPdive

  Search the live web and get back answer-ready page content, not a list of links. Each result carries the actual text of the page (url, title, date, content), already extracted, cleaned and trimmed for LLM use, so facts can be quoted and cited straight from the response. Use it for anything that needs current or post-training information: news, prices, releases, docs, sports, niche facts. Write the query the way a person would type it, in any language: localization is automatic. The 'mako' model (default) returns the fact-carrying sentences of each page, fast and concise, right for most questions. The 'moby' model returns the full readable text of each page: use it for deep research or when complete context matters. Set answer=true to also get a direct answer synthesized from the sources.

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• search

  Melvea Local Honey Discovery

  Search the Melvea local honey directory by free-text query and return matching producers as a list of results (id, title, url). Designed for ChatGPT Deep Research and Company Knowledge. Use for any local-honey discovery query that names or implies a place; the tool parses place and varietal from the query. Returns an honest empty list when nothing matches — never fabricate. Pair with fetch to retrieve full producer detail.

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• lion_deep_research

  LION — Verified Company & Compliance Data

  Company research for AI agents $0.03: web + scrape + firmographics + domain trust in one call. Alias: /api/x402/company-research. Ed25519-attested. Use before/after people enrichment. Upsell verified company file $0.95. ?q=company or domain. [x402 paid: GET /api/x402/deep-research-json price $0.03 on Base]

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• research_task

  DC Hub — Data Center Intelligence MCP Server

  Commission an ASYNC, CITED research dossier from DC Hub's corpora (news, deals, facilities, market deep-dive narratives + live market components) — a decision-ready analyst brief with [n] citations, not a lookup. Requires a key (one claim_free_key call), 5 dossiers/day. Submits the question, waits up to ~35s for completion, and returns the finished dossier inline when ready; if still running, returns {task_id} — call research_task task_id=<id> to fetch it. Params: question (required for a new dossier, min 12 chars) OR task_id (poll an earlier one). Typical completion under a minute. Try: research_task question="What do recent deals say about gas-bridged power for data centers in ERCOT?". Do NOT use for a single fact (use search_intelligence / semantic_search); this synthesizes ACROSS sources with citations.

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• slovenia-cadastral-deep

  geoagentic

  Deep parcel and building analysis for Slovenia using GURS WFS data. Returns zoning, actual use, heritage protection, road access, buildings on parcel, and utilities. USE FOR: - "Analyze parcel 3086 in Ljubljana center" - "Find buildable parcels ~500m² in Ljubljana" - "What buildings are on this parcel?" - "Find parcels near these coordinates" - "Get full details on building 1234" NOT FOR: simple parcel lookup → use slovenia-cadastre instead (faster, lighter). NOT FOR: spatial/zoning map queries → use slovenia-wfs-expert instead. SEARCH MODES — pick ONE per call: 1. PARCEL BY NUMBER (requires --parcel AND --ko) → --parcel 3086 --ko 1725 2. LOCATION SEARCH (requires --lat AND --lon, or --location) → --lat 46.058 --lon 14.501 --radius 100 → --location "Tivoli Park Ljubljana" --radius 200 3. BUILDING BY NUMBER (requires --building, optionally --ko) → --building 1234 --ko 1728 4. COMMUNITY SEARCH (requires at least --community or --size) → --community LJUBLJANA --size 500 --buildable COMMON KO IDs: 1725 = Ljubljana center 1728 = Ljubljana Šiška 1740 = Ljubljana Bežigrad 2131 = Maribor NOTE: This tool makes multiple WFS calls per result and can be slow (10-30s). Use --limit to keep response times reasonable.

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• fonteum_lookup_contractor_history

  Fonteum — Healthcare Provider Data (Hosted)

  Look up a US federal contractor record as of a requested date using UEI or CAGE. Retained history is currently limited to USASpending award rows; other fields may be current, absent, or nullable. Returns dated source context. Use for historical award research or contractor change questions.

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• research

  Sofya

  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 25 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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• archive_provenance

  Colour Memory

  Explains the provenance of a named archive colour: documented fact vs computational derivation vs cultural interpretation, with confidence and citation format. This is one component of colour_passport, but also a standalone research tool for deep provenance work (museum, documentary, editorial). Use colour_passport for a general profile; call this directly for research workflows needing full source-chain detail.

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• search_research

  Heista

  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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• verify_references

  OpenWarrant — Document Verification Suite

  Fact-check a document's REFERENCES and CLAIMS — built for AI-generated reports whose citations must be checked before they're trusted. USE THIS WHEN someone shares a report, article, whitepaper, or deep-research export (or a link to one) and asks: is this accurate / legit? are these citations real? fact-check this. did the AI make this up? Also use it proactively before relying on any AI-written document. Provide the document ONE way: `url` (a public http(s) link to a PDF or web page — fetched server-side, the cheapest call: no need to download or encode anything), `text` (pasted markdown/plain prose), OR `bytes_b64` (a base64 PDF; URLs are read from the PDF's link annotations, so they're exact). Default (fast): provenance (is it a ChatGPT deep-research export?), citation resolution (live / archived / dead, papers matched against arXiv/Crossref to catch 'real ID, wrong paper'), and internal MATH (recompute the doc's own arithmetic). Set `deep=true` to also fetch each cited source and judge whether it SUPPORTS or CONTRADICTS the claim (slower, ~a minute). Returns a trust summary, per-item tables, and a shareable `permalink` to the public fact-check record. HONEST BOUNDARY: this reports verification COVERAGE, not truth — 'supported' means evidence-backed (not necessarily true) and 'unsupported' means no evidence found (not necessarily false). It tells a reviewer WHERE to look; it does not bless the document, and it never affects the fraud risk band.

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• search

  DC Hub — Data Center & Power Intelligence

  Search DC Hub for relevant records (OpenAI Deep Research / ChatGPT connector format). Returns a list of matching data-center facilities as {id, title, url}; pass an id to the `fetch` tool for the record, or open the url to cite the live facility page. For structured queries (by MW, operator, status, market) use search_facilities directly.

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• fetch

  DC Hub — Data Center & Power Intelligence

  Fetch a DC Hub record for an id returned by the `search` tool (OpenAI Deep Research / ChatGPT connector format). Returns {id, title, text, url, metadata} — a citable public summary of one data-center facility (name, operator, location, status, market). For full structured specs (capacity MW, coordinates) use get_facility or open the url.

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• web_research

  dialogbrain

  Answer a research question from live web sources in one call — returns a synthesized answer with numbered [N] citation markers and a citations array of {url, title, index}. Supports recency and domain filters. Use for questions needing current, sourced information (news about a company, market state, comparisons). For raw search result links use web.search; mode='deep' runs minutes-long exhaustive research — only when explicitly requested.

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• web_search

  Teardrop

  Real-time web search via Tavily. Use for current events, fact-checking, and research. Set search_depth='advanced' for complex research queries (higher quality, higher cost). Set topic='news' for recent headlines or 'finance' for market information.

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• adw.adw_618

  AlpineDataWorks Intelligence Server

  Returns a 0-100 frontier-hardware research-momentum score (Semantic Scholar publication counts for quantum computing, solid-state batteries, and neuromorphic computing — 90-day windows vs. baseline, summed) with trend, z_score, per_variant_w0, and window_counts. Call when the user asks about emerging-tech R&D acceleration, quantum/battery/neuromorphic research trends, or pre-patent signals, or when timing deep-tech investment, corporate R&D strategy, or technology-scouting decisions. Updates: daily.

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• research_task

  DC Hub — Data Center & Power Intelligence

  Commission an ASYNC, CITED research dossier from DC Hub's corpora (news, deals, facilities, market deep-dive narratives + live market components) — a decision-ready analyst brief with [n] citations, not a lookup. Requires a key (one claim_free_key call), 5 dossiers/day. Submits the question, waits up to ~35s for completion, and returns the finished dossier inline when ready; if still running, returns {task_id} — call research_task task_id=<id> to fetch it. Params: question (required for a new dossier, min 12 chars) OR task_id (poll an earlier one). Typical completion under a minute. Try: research_task question="What do recent deals say about gas-bridged power for data centers in ERCOT?". Do NOT use for a single fact (use search_intelligence / semantic_search); this synthesizes ACROSS sources with citations.

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• get_report_result

  Moonlings — Local Business Intelligence

  Fetch a completed deep research report by slug. Free. Returns the long-form report (markdown), a structured-findings JSON block, the action list, and the report card. If the report is still generating you get its status instead; if it failed, the launch charge is refunded automatically.

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