306,437 tools. Last updated 2026-07-25 10:29
"Methods to Enhance Deep Research Capabilities" matching MCP tools:
- Buy credits for the edge library and AI research. Default $5 minimum. Free — no credits consumed to call this. TWO PAYMENT METHODS: card (default): Returns a Stripe Checkout link for your user to click and pay. After payment, call check_balance to confirm credits were added. crypto: USDC on Base. Fully autonomous — no human needed. Three steps: 1. buy_credits(payment_method='crypto') → returns deposit address + payment_intent_id 2. Send USDC to the deposit address (use your wallet tool) 3. buy_credits(payment_intent_id='pi_...') → confirms payment, credits added instantly If you have wallet access, this is the fastest path — fully machine-to-machine.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
- Upscales and enhances an image — sharpens edges, denoises, and raises resolution by an optional scale factor. Auto-picks the newest enabled Picsart upscale / enhance model unless overridden via the `model` param. Use this when the user asks to "upscale", "enhance", "make it higher resolution", "sharpen", "clean up this photo", or "make this 4k". Do NOT use this to remove the background (use `picsart_remove_bg`), replace the background (use `picsart_change_bg`), convert raster to SVG (use `picsart_vectorize`), or generate a new image (use `picsart_generate`). Required input: `image` — a publicly-accessible URL, not a local file path. Optional: `model` to pin a specific enhance model, `scaleFactor` (e.g. 2 or 4) for upscale ratio. Example: `{ image: "https://example.com/photo.jpg", scaleFactor: 4 }`. Returns `{ assets, id, model, created_at, summary, why_relevant, url, results: [{ url, metadata? }], drive? }` plus a `resource_link` block per result URL. `id` is the SDK's generation handle; `metadata` may include model-specific tags. Spends credits. Requires Authorization: Bearer <picsart_token>.Connector
- Active website security scan: runs the ContrastScan C engine (11 modules — HTTP security headers, SSL/TLS, DNS, redirect chain, information disclosure, cookie flags, DNSSEC, HTTP methods, CORS, HTML hygiene, deep CSP analysis) against the live site and enriches the raw result with severity-ranked vulnerability findings and a letter grade. Use for a hands-on misconfiguration scan; use audit_domain for passive recon (DNS/WHOIS/SSL/threat intel) and scan_headers for headers only. Active outbound fetch — a per-target eTLD+1 throttle (60 req/min) applies. Free: 30/hr (costs 6 tokens), Pro: 500/hr. Returns {domain, resolved_ip, total_score, max_score, grade, findings, findings_count, headers, ssl, dns, redirect, disclosure, cookies, dnssec, methods, cors, html, csp_analysis, enterprise, summary, next_calls}.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
- 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]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
- Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.Connector
- Restore and enhance faces in an image using GFPGAN. Detects all faces via RetinaFace, restores quality (fixes blur, noise, compression artifacts), and pastes them back. Optionally enhances the background using Real-ESRGAN. GPU-accelerated, sub-3s latency. Args: image_base64: Base64-encoded image data containing faces (PNG, JPEG, WebP). upscale: Output upscale factor -- 1 to 4 (default: 2). enhance_background: Whether to enhance background with Real-ESRGAN (default: true). Returns: dict with keys: - image (str): Base64-encoded restored image - format (str): Output image format - width (int): Output width - height (int): Output height - upscale (int): Scale factor applied - processing_time_ms (float): Processing time in millisecondsConnector
- Returns a summary of all Carbone capabilities: supported formats, features, tool usage examples, and links to full documentation. Call this first if you are unsure what Carbone can do.Connector
- Lists every registered jurisdiction with its code, active/inactive status, and supported capabilities — search, entity lookup, quick verification, and deep verification. Free and requires no authentication. Use it to confirm a state or country is supported and which verification tiers it offers before calling verify_business or search_entities.Connector
- Get a Stripe Billing Portal URL for the human to manage their subscription — update payment methods, view invoices, change plans, or cancel. Requires an existing Stripe subscription.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
- 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
- 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
- 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
- Search across your own connected-account content and return the best matches. Each result has an `id` (pass it to `fetch` for the full item), a `title`, a `url`, and a `text` snippet. This is the deep-research "search" entrypoint the ChatGPT/Claude connectors call by convention; for semantic search over analyzed videos specifically use `search_videos`. Returns {"results": [...]}; when you have no connected accounts it returns reason="no_connected_accounts" plus a connect_url instead of results.Connector
- Search official economic statistics by free text, e.g. 'inflation barbados' or 'government debt japan'. Returns result ids that can be passed to fetch. Designed for deep-research connectors; for richer control use get_indicator / get_series.Connector