"A general search for research-related information" matching MCP tools:

• brave_summarizer

  brave

  Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.

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• createDeepResearch

  Parallel Task MCP

  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.

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• nexbid_search

  discovery

  <tool_description> Search and discover products, recipes AND services in the Nexbid marketplace. Nexbid Agent Discovery — search and discover advertiser products through an open marketplace. Returns ranked results matching the query — products with prices/availability/links, recipes with ingredients/targeting signals/nutrition, and services with provider/location/pricing details. </tool_description> <when_to_use> Primary discovery tool. Use for any product, recipe or service query. Use content_type filter: "product" (only products), "recipe" (only recipes), "service" (only services), "all" (all, default). For known product IDs use nexbid_product instead. For category overview use nexbid_categories first. </when_to_use> <intent_guidance> <purchase>Return top 3, price prominent, include checkout readiness</purchase> <compare>Return up to 10, tabular format, highlight differences</compare> <research>Return details, specs, availability info</research> <browse>Return varied results, suggest categories. For recipes: show cuisine, difficulty, time.</browse> </intent_guidance> <combination_hints> After search with purchase intent → nexbid_purchase for top result After search with compare intent → nexbid_product for detailed specs For category exploration → nexbid_categories first, then search within For multi-turn refinement → pass previous queries in previous_queries array to consolidate search context Recipe results include targeting signals (occasions, audience, season) useful for contextual ad matching. </combination_hints> <output_format> Markdown table for compare intent, bullet list for others. Products: product name, price with currency, availability status. Recipes: recipe name, cuisine, difficulty, time, key ingredients, dietary tags. Services: service name, provider, location, price model, duration. </output_format>

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• fda_search_recall_text

  FDA Data MCP

  Full-text search across recall reasons and product descriptions using PostgreSQL text search. Finds recalls mentioning specific terms (e.g. 'salmonella contamination', 'mislabeled', 'sterility'). Supports multi-word queries ranked by relevance. Filter by classification, product_type, or date range. Related: fda_search_enforcement (search by company name, classification, status), fda_recall_facility_trace (trace a recall to its manufacturing facility).

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• searchPapers

  qubitsok — Quantum Computing Jobs, Papers & Researchers

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

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• fetch

  Dynamoi

  OpenAI ChatGPT Deep Research / Connectors fetch contract. Given an id returned by `search` (formatted as 'artist:<uuid>', 'campaign:<uuid>', or 'smartlink:<uuid>'), returns the full record for citation.

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• reset_session

  VARRD — Statistically Validated Trading Edges + AI Research Engine

  Kill a broken research session and start fresh. Use this when a session gets stuck, produces errors, or enters a bad state. Free — no credits consumed. After resetting, call research without a session_id to start a new clean session.

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• shippingrates_search

  freightgate-mcp-server

  Search the ShippingRates database by keyword — matches against carrier names, port names, country names, and charge types. Use this for exploratory queries when you don't know exact codes. For example, search "mumbai" to find port codes, or "hapag" to find Hapag-Lloyd data coverage. Returns matching trade lanes, local charges, and shipping line information. FREE — no payment required. Returns: { trade_lanes: [...], local_charges: [...], lines: [...] } matching the keyword. Related tools: Use shippingrates_port for structured port lookup by UN/LOCODE, shippingrates_lines for full carrier listing.

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• atlas_advisor_chat

  CareerProof MCP

  Send a message to Atlas Advisor for lightweight hiring advice (2 credits). Faster and cheaper than atlas_chat, no tool use -- best for general hiring questions. Returns AI response text and a conversation_id. Omit conversation_id to start a new conversation; include it to continue a thread.

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• fetch_open_tickets

  Sweeppea MCP

  Get open support tickets with pagination (20 per page) and search. Returns summary info only — use get_ticket with the case number to get full ticket details including notes, files, and collaborators. Supports filtering by subject/description text, platform (renaissance, api, general, overture, winners), and priority (1=Low, 2=Medium, 3=High). # fetch_open_tickets ## When to use Get open support tickets with pagination (20 per page) and search. Returns summary info only — use get_ticket with the case number to get full ticket details including notes, files, and collaborators. Supports filtering by subject/description text, platform (renaissance, api, general, overture, winners), and priority (1=Low, 2=Medium, 3=High). ## Parameters to validate before calling - search (string, optional) — Search by subject or description (case-insensitive) - platform (string, optional) — Filter by platform: renaissance, api, general, overture, winners - priority (number, optional) — one of: 1, 2, 3 — Filter by priority: 1 (Low), 2 (Medium), 3 (High) - page (number, optional) — Page number for pagination (default: 1, 20 tickets per page)

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• fda_drug_labels

  FDA Data MCP

  Search FDA Structured Product Labeling (SPL) data — full drug package inserts. Filter by drug name, manufacturer, application number, or specific label section (e.g., indications_and_usage, warnings, adverse_reactions, boxed_warning). Returns complete label text for matching sections. Related: fda_search_drugs (application-level data), fda_search_ndc (NDC product details).

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• fetch_closed_tickets

  Sweeppea MCP

  Get closed/resolved support tickets with pagination (20 per page) and search. Returns summary info only — use get_ticket with the case number to get full ticket details including notes, files, and collaborators. Supports filtering by subject/description text, platform (renaissance, api, general, overture, winners), and priority (1=Low, 2=Medium, 3=High). # fetch_closed_tickets ## When to use Get closed/resolved support tickets with pagination (20 per page) and search. Returns summary info only — use get_ticket with the case number to get full ticket details including notes, files, and collaborators. Supports filtering by subject/description text, platform (renaissance, api, general, overture, winners), and priority (1=Low, 2=Medium, 3=High). ## Parameters to validate before calling - search (string, optional) — Search by subject or description (case-insensitive) - platform (string, optional) — Filter by platform: renaissance, api, general, overture, winners - priority (number, optional) — one of: 1, 2, 3 — Filter by priority: 1 (Low), 2 (Medium), 3 (High) - page (number, optional) — Page number for pagination (default: 1, 20 tickets per page)

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• contacts-create_research

  Saber

  Start a contact research job — AI gathers insights from LinkedIn and other sources asynchronously; use contacts.get_research to poll for results

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• bing_search

  Searchapi

  Search the web using Bing. Returns organic results, related searches and more. Alternative to Google for web search with different ranking algorithms and results.

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• microsoft_code_sample_search

  Microsoft Learn MCP

  Search for code snippets and examples in official Microsoft Learn documentation. This tool retrieves relevant code samples from Microsoft documentation pages providing developers with practical implementation examples and best practices for Microsoft/Azure products and services related coding tasks. This tool will help you use the **LATEST OFFICIAL** code snippets to empower coding capabilities. ## When to Use This Tool - When you are going to provide sample Microsoft/Azure related code snippets in your answers. - When you are **generating any Microsoft/Azure related code**. ## Usage Pattern Input a descriptive query, or SDK/class/method name to retrieve related code samples. The optional parameter `language` can help to filter results. Eligible values for `language` parameter include: csharp javascript typescript python powershell azurecli al sql java kusto cpp go rust ruby php

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• search

  Dynamoi

  OpenAI ChatGPT Deep Research / Connectors search contract. Returns matching Dynamoi artists, campaigns, and Smart Links so they can be cited in a deep-research session. For regular ChatGPT chat use dynamoi_search instead.

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• search_local_experiences

  tickadoo-mcp

  Use this when the user mentions a place, neighbourhood, landmark, or area but does not give exact coordinates. Examples: 'near the Louvre', 'in Trastevere', 'around Times Square', "walking distance from St Paul's Cathedral". Returns experiences matched first by exact venue/neighbourhood, then by city centre fallback. Do not use for general city-wide search; use search_experiences for that.

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• web_search

  qsearch

  Search the web via Brave Search API with local QVAC LLM cleaning. Returns cleaned markdown summaries. Use for general web research, factual lookups, and topic exploration.

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• brave_web_search

  brave

  Performs web searches using the Brave Search API and returns comprehensive search results with rich metadata. When to use: - General web searches for information, facts, or current topics - Location-based queries (restaurants, businesses, points of interest) - News searches for recent events or breaking stories - Finding videos, discussions, or FAQ content - Research requiring diverse result types (web pages, images, reviews, etc.) Returns a JSON list of web results with title, description, and URL. When the "results_filter" parameter is empty, JSON results may also contain FAQ, Discussions, News, and Video results.

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• fda_drug_shortages

  FDA Data MCP

  Search FDA drug shortages by generic name, company, status, or availability. Drug shortages signal manufacturing capacity strain, quality issues, or supply chain disruption. Useful for identifying companies with operational challenges. Related: fda_search_drugs (drug application data by company), fda_search_ndc (NDC-level product details).

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