"An exploration of deep search technologies or methods" matching MCP tools:

• microsoft_docs_fetch

  Microsoft Learn MCP

  Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.

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• search

  UseMyContext

  Search the user's files by filename and return matching documents in the deep-research result shape. ALIAS: this is the SAME search as search_files (same data, same permissions) - use it when your client requires the id/title/url search contract (ChatGPT deep research); otherwise prefer search_files for richer file metadata. Each result's id can be passed to fetch (or get_file) to read that document. Read-only; always allowed.

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• scaffold_service

  @imqueue

  READ-ONLY: returns generated source code as text and writes nothing to disk, creates no project and runs no command. Generates an idiomatic @imqueue/rpc service (an IMQService subclass with @expose()d, JSDoc-typed methods) plus a bootstrap that starts it. Provide the methods you want, or omit them for a starter template. Any non-primitive parameter or return type also gets a types.ts with the required @classType()/@property() declarations — without those the generated client types it `any`, which compiles. Use create_service (local install only) if you want files actually written.

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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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• search

  StatCite — Verified Economic Statistics

  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.

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• get_vehicle_booking_link

  gondola

  Get a Gondola.ai deep link for a specific vehicle from search results. Returns a link to Gondola's checkout for this vehicle, where the traveler reviews the rate and completes the reservation on the web. This is the booking path for this connection. (Connections belonging to an approved booking partner — which requires the mcp:book OAuth scope — additionally get an in-conversation option here.) Args: search_id: Search ID from search_vehicles. vendor_code: Vendor code from search results. rate_code: Rate code of the selected vehicle. pickup_datetime: Pickup date and time in ISO format. dropoff_datetime: Drop-off date and time in ISO format. Returns: Booking instructions tailored to the user's auth status.

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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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• explore_topic

  OpenArx

  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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• get_slide_deep_dive

  flevy

  Slide-by-slide preview of a Flevy document (a "doc-<n>" content_id). Returns every showcased slide with its name, a text description of what the slide contains (you cannot see the image, so use the description), a preview image URL, and a deep link to that slide on flevy.com. Use when a user wants to know what is inside a specific presentation before purchasing, or to reference an individual slide. Only some documents have deep dives; get_content_details reports the count.

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• tech_fingerprint

  ContrastAPI

  Detect website technology stack: CMS, frameworks, CDN, analytics tools, web servers, languages (via HTTP headers + HTML analysis). Use for passive reconnaissance; for full audit use audit_domain. Free: 30/hr, Pro: 500/hr. Returns {technologies: [{name, category, confidence%, version}]}.

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• osint_investigate_infrastructure_start

  dns

  Start an async deep-infrastructure OSINT investigation for a query (domain, IP, or org). Operator-deploy only; degrades to info when unprovisioned. Returns an investigationId immediately — poll with osint_investigation_status.

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• audit_stack

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.

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• t54_list_operations

  Agentic Swarm Marketplace (T54 x402 MCP)

  Returns operationIds, HTTP methods, paths, and query parameter names from the bundled OpenAPI spec (no network). Use before t54_x402_request or per-SKU tools.

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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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• get_full_text

  Pubmed

  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.

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• get_skill_clusters

  nü people Job Server

  Returns the current skill cluster data for public jobs on the nü people website. Use this tool when the user wants an overview of which skills or technologies are currently in demand.

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• get_services

  Andrii Co. Notary & Apostille Assistant

  The full service catalog (Washington State notary & apostille) with prices and the accepted payment options. Optional — the server instructions already summarize the flow; call this when the customer asks about services or payment methods.

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• run_panel_study

  Minds: Synthetic Market Research Panels

  **Call this tool only after the user explicitly confirms the exact draft revision returned by plan_panel_study.** This is the execution boundary. Never infer confirmation from silence, from the original request, or from your own suggested answer. If the user changes intent, source, questions, methods, or outputs, revise the draft with plan_panel_study first. Set advancedMethodOptIn only when the user explicitly chose the advanced method. The server reloads the stored draft, marks required capabilities reviewed, validates method versions/runners/configuration, and refuses unavailable methods. Treat list_research_methods and server validation as the availability authority; execute only methods reported with executable:true.

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• provider_profile

  telecom-truth

  Canonical profile of a US internet provider by name (handles brand variants, e.g. 'ATT', 'Google Fiber'). Returns the canonical identity, FCC registration numbers, technologies filed, the live profile URL, and — when precomputed — an answer pack of grounded sections (overview, coverage, measured-vs-claimed speeds, competition, recent signals, trajectory). Use it to disambiguate providers before making claims about them.

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• audit_domain

  ContrastAPI

  Perform comprehensive domain audit: combines domain_report + live HTTP security headers + technology fingerprinting. By default report.dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and report.dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use when you need the full picture (recon + active checks); use domain_report for passive-only assessment. Response carries next_calls — chain with subdomain_enum (always emitted) and ssl_check (when an A record resolves) for the residual recon depth (tech_fingerprint already inline as `technologies`). Free: 30/hr (costs 6 tokens), Pro: 500/hr. Returns {domain, report, technologies, live_headers, summary, next_calls}.

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