305,560 tools. Last updated 2026-07-23 07:10
"Using NotebookLM for deep research" matching MCP tools:
- 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
- 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.Connector
- 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.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
- What do we know about this company? $0.012 one-call research for AI agents: web + scrape + firmographics + domain trust. Ed25519-attested. ?q=company or domain. Matches: company research, KYC taster, firmographics deep dive. Upsell compliance pack $0.05. [x402 paid: GET /api/x402/deep-research-json price $0.012 on Base]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
Autonomous buy-side research: diligence, earnings, SEC filings, comp sets. Source-cited real data.
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
- 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
- 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.Connector
- 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.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
- 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.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
- 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
- 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.Connector
- 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.Connector
- 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.Connector
- 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.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