282,056 tools. Last updated 2026-07-10 11:58
"A service for finding research papers and literature on deep learning" matching MCP tools:
- 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.Connector
- Search machine-learning / AI research papers (via Hugging Face Papers, the successor to Papers with Code). Returns arXiv id, title, authors, community upvotes, and a linked GitHub repo when available. Use for "papers on <topic>", "recent ML research about X".Connector
- 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.Connector
- [Academic Research] Search Google Scholar for academic papers. Returns titles, authors, citations, and links. Args: query: Search query (e.g. 'deep learning medical imaging') max_results: Maximum papers to return (default 10)Connector
- 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".Connector
- 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.Connector
Matching MCP Servers
- Alicense-qualityCmaintenanceEnables deep research tasks using a multi-agent architecture that integrates any LLM and MCP tools. Available via MCP stdio, streamable HTTP, and SSE transports.Last updated17MIT
- Flicense-qualityFmaintenanceEnables AI assistants to perform deep web research and generate comprehensive reports using a multi-agent divide and conquer approach.Last updated1
Matching MCP Connectors
Conduct comprehensive research projects using a virtual computer equipped with a real browser, coding tools, document creation capabilities, and more. Deep Research by Openhelm enables your agent to tackle work such as: • Market and competitor analysis • Industry and company research • Investment and acquisition due diligence • Technical and scientific investigations • Report generation with sources and evidence What makes OpenHelm the best solution for this: • Research is continuously revie
The verified hub for conferences and journals. Powered by AI to match your scholarly ambitions with the world's most prestigious academic opportunities.
- Get upcoming vessel arrivals and departures at a specific port. Use this to check what vessels are expected at a port — useful for booking planning and tracking. Returns vessel names, carriers, ETAs/ETDs, and service routes. For transit time estimates between two ports, use shippingrates_transit. For detailed service-level routing, use shippingrates_transit_schedules. PAID: $0.02/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { vessel_name, carrier, voyage, eta, etd, service, from_port, to_port }.Connector
- Get upcoming vessel arrivals and departures at a specific port. Use this to check what vessels are expected at a port — useful for booking planning and tracking. Returns vessel names, carriers, ETAs/ETDs, and service routes. For transit time estimates between two ports, use shippingrates_transit. For detailed service-level routing, use shippingrates_transit_schedules. PAID: $0.02/call via x402 (USDC on Base or Solana). Without payment, returns 402 with payment instructions. Returns: Array of { vessel_name, carrier, voyage, eta, etd, service, from_port, to_port }.Connector
- 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.Connector
- ONE-CALL attested company/crypto deep research. Pass ?q=<company, domain, or topic> (and optional ?domain=, ?num=, ?receipt=1). LION runs web search -> scrapes the top source -> firmographics enrich (Wikidata + SEC) -> domain trust, and merges them into one Ed25519-attested JSON — replacing StableEnrich's 3-4 call research loop (~$0.08) with a single $0.012 call (~85% cheaper). For company research, vendor due diligence, business intelligence, SEC financials, and crypto/token research. Keyless, no account, no PII. For people/email/LinkedIn/maps use stableenrich.dev — LION proves companies. Volume: ?volume=100 -> $0.010, ?volume=1000 -> $0.008. [x402 paid tool: GET /api/x402/deep-research-json?src=mcp returns the 402 challenge with the canonical payTo; price 0.012 USDC on Base eip155:8453.]Connector
- Autonomous web research agent. This is a separate AI agent layer that independently browses the internet, searches for information, navigates through pages, and extracts structured data based on your query. You describe what you need, and the agent figures out where to find it. **How it works:** The agent performs web searches, follows links, reads pages, and gathers data autonomously. This runs **asynchronously** - it returns a job ID immediately, and you poll `firecrawl_agent_status` to check when complete and retrieve results. **IMPORTANT - Async workflow with patient polling:** 1. Call `firecrawl_agent` with your prompt/schema → returns job ID immediately 2. Poll `firecrawl_agent_status` with the job ID to check progress 3. **Keep polling for at least 2-3 minutes** - agent research typically takes 1-5 minutes for complex queries 4. Poll every 15-30 seconds until status is "completed" or "failed" 5. Do NOT give up after just a few polling attempts - the agent needs time to research **Expected wait times:** - Simple queries with provided URLs: 30 seconds - 1 minute - Complex research across multiple sites: 2-5 minutes - Deep research tasks: 5+ minutes **Best for:** Complex research tasks where you don't know the exact URLs; multi-source data gathering; finding information scattered across the web; extracting data from JavaScript-heavy SPAs that fail with regular scrape. **Not recommended for:** - Single-page extraction when you have a URL (use firecrawl_scrape, faster and cheaper) - Web search (use firecrawl_search first) - Interactive page tasks like clicking, filling forms, login, or navigating JS-heavy SPAs (use firecrawl_scrape + firecrawl_interact) - Extracting specific data from a known page (use firecrawl_scrape with JSON format) **Arguments:** - prompt: Natural language description of the data you want (required, max 10,000 characters) - urls: Optional array of URLs to focus the agent on specific pages - schema: Optional JSON schema for structured output **Prompt Example:** "Find the founders of Firecrawl and their backgrounds" **Usage Example (start agent, then poll patiently for results):** ```json { "name": "firecrawl_agent", "arguments": { "prompt": "Find the top 5 AI startups founded in 2024 and their funding amounts", "schema": { "type": "object", "properties": { "startups": { "type": "array", "items": { "type": "object", "properties": { "name": { "type": "string" }, "funding": { "type": "string" }, "founded": { "type": "string" } } } } } } } } ``` Then poll with `firecrawl_agent_status` every 15-30 seconds for at least 2-3 minutes. **Usage Example (with URLs - agent focuses on specific pages):** ```json { "name": "firecrawl_agent", "arguments": { "urls": ["https://docs.firecrawl.dev", "https://firecrawl.dev/pricing"], "prompt": "Compare the features and pricing information from these pages" } } ``` **Returns:** Job ID for status checking. Use `firecrawl_agent_status` to poll for results.Connector
- 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. For US court opinions and case law, use google_scholar_cases instead.Connector
- 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.Connector
- 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.Connector
- 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.Connector
- 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.Connector
- 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.Connector
- Act on a signal finding — the exit from discovery into the lead repository (VAA-100). action='find_people' (default) runs a paid Exa search (≤5¢) for decision-makers at the finding's company and upserts them into `gtm_leads` with source 'signal' and the signal headline as their hook/why; action='dismiss' marks the finding handled without spending. Both stamp acted_at so a finding is handled once (a second find_people returns already_acted). Pass `finding_id` (from `worker_findings` or the Workers page's buying-signals feed) and optionally `roles` to steer who to look for (default founder/CEO/CTO/Head-of/VP). Returns { ok, action, found, added, charged_cents }.Connector
- 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.Connector
- 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.Connector