"Information or topic related to 'context7'" matching MCP tools:

• methodology_faqA

  RefproA

  Answer questions about how Refpro works. Use this when a user asks what Refpro is, what lender-grade means, which deal types are supported, what a deal pack contains, which output formats are available, how the pricing tiers work, or how Refpro compares to spreadsheets and free calculators. Input: query, a free-text question or topic keyword, 200 characters maximum. Returns a 2 to 4 sentence answer, a list of related topic titles, and a canonical source URL on refpro.ai. Backed by a static curated knowledge base, so answers are stable, quotable and identical every time: no LLM-generated text and no network calls. Falls back to a general Refpro overview when the query matches no known topic. Do not use to analyze a deal or compute numbers; call deal_quick_check for that. Do not use for general real estate investing questions; this tool covers Refpro itself.

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• get_elt_recyclingA

  Trustbase Lab · Trusted Data Infrastructure for the AI EraA

  Purpose: the end-of-life tyre (ELT) collection topic for the rCB supply chain - policy targets, the 2026 standard system, the collection-network build-out, the processing value ladder (retreading > recycled rubber > pyrolysis) and market-size figures, with official sources labelled per record. Guidelines: call with no arguments for the full topic, or pass topic (policy/standard/network/ladder/market) to focus one section; pair with get_elt_price_index (category G-ELT) for collection prices and get_feedstock_overview for arisings. Limits: policy and standard records are sourced from official documents; market-size figures are public-information compilations and labelled as such; does not return prices. Ex: 'China end-of-life tyre recycling policy 2030 target', 'ELT collection network China', 'scrap tyre pyrolysis standards 2026'.

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• awareness_search_knowledgeA

  Awareness — Memory, Knowledge & Agent MarketsA

  Search the PUBLIC Awareness knowledge graph — 1,000+ sourced scientific papers. Use this when the user (especially a researcher or a research agent) asks for peer-reviewed findings: "latest evidence on X", "highly-cited papers about Y", "what does the literature say about Z". This is NOT a memory tool — it does not read the user's private memory, and it needs no per-user API key, so it works even when Awareness memory is not configured (the MCP connection itself still needs its own token). Coverage: condensed-matter, quantum-information, materials, machine-learning, renewable-energy, chemistry, biology, medicine, neuroscience, computer-science (2022-2026, cited >= 50), plus a business supply/demand domain. What comes back (anonymous preview mode): title, snippet (preview), confidence, source_ref (DOI), citation_count, domain/topic, and a detail page URL. Full content is NOT in the preview — tell the user it is paid retrieval via POST /api/v1/public/retrieve with an API key (see https://awareness.market/en/knowledge for the KaaS product). Args: query: Natural-language research question or keywords (2-1000 chars). domain: "science" (default) or "business". topic: Optional sub-topic slug, e.g. "medicine", "condensed-matter", "supply-chain". Empty = search all topics in the domain. top_k: Max results, 1-20 (default 5; anonymous callers are capped at 3). include_graph: When true, also return same-topic related items. Returns: {"results": [{id, title, snippet, confidence, source_ref, citation_count, domain, topic, url}], "related": [...], "total": int, "mode": str} `mode` is "preview" (anonymous) or "full" (authenticated).

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• web_searchA

  TeardropA

  Real-time web search via Tavily for current events, fact-checking, and research. Use search_depth='advanced' for complex queries (higher quality, higher cost) and topic='news' or 'finance' for headlines or market information. Use when: Choose when the task needs current, external, or factual information not available from on-chain or local data — e.g. news, prices, documentation, or fact-checking. Limitations: Returns web snippets, not raw page bodies; results depend on Tavily coverage. Advanced depth costs more. Not a substitute for on-chain tools like get_token_price. Alternatives: get_token_price, http_fetch

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• get_truss_informationA

  TrussA

  Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.

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• research_bulkA

  trendflowA

  Researches up to 10 topics in a single call, each with the same full picture as `research_trend`: interest over time, where it is most searched, and related queries. Each topic is looked up on its own scale, so they are not comparable to one another. Use this when you need data across many topics — a long or rich research pass — instead of one tool call per topic. Each section is fetched independently, so a partial result is normal: any section that fails carries an `error` instead of data and the rest still returns.

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• nevent_helpA

  Nevent MCPA

  Get guidance when you are unsure which tool to call or how to handle an error. Returns structured markdown for the requested topic. Call with topic="workflows" for common patterns, topic="errors" for error code meanings, topic="tenants" for multi-tenant guidance, or topic=<category> like "paid_media" / "analytics" / "segments". Omit topic to get an index of all available topics.

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• fetch_latest_syntaxA

  Titan Frameworks (LangChain, LlamaIndex, Ollama, XRPL)A

  Curated working imports, snippets, and migration notes for one topic (agents, rag, wallet, payment, trustlines, …). Use when writing or migrating a fragment; unknown topics fall back to related doc chunks instead of failing. Prefer fetch_working_example for a complete runnable file, search_ai_framework_docs for open-ended lookup, and diagnose_framework_error for exceptions. Paid tools/call: $0.001 USDC or 1000 drops XRP; read-only catalog.

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• find_related_social_media_profilesA

  Viral OutliersA

  Discover new creators on TikTok, Instagram or YouTube from one seed: a creator handle (walks who they follow on TikTok, who they collaborate with, tag or mention on Instagram), a hashtag, or a search phrase. Every account surfaced is screened live with one provider call (recent posts: how many reached 5k+ views, posting activity, newest post) and judged for topic relevance from its bio and captions, then returned ranked with recommended = qualified and on-topic. Accounts already in the database come back flagged known with their profileId. Nothing is crawled or added automatically: call crawl_profile for the ones you want. Priced up to 25 credits per call: 5 base plus 1 per account actually screened, the rest refunded (nothing found = fully refunded). Pass topic to say what related means for your use case. Not for looking up a known creator (use search_profiles). Cost: 25 credits per call.

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• kenwea.recommendations.listRelatedProductsA

  Kenwea — Sandbox Attestation & Agent MarketplaceA

  List up to 20 products related to one product, each with the reason it is related. productId is a product id from kenwea.marketplace.search; an unknown id returns an empty list rather than an error. Public and readable by unclaimed agents. For open-ended discovery by text, category or price use kenwea.marketplace.search.

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• get_topic_trends_attentionA

  Tanod WebA

  utilpeek: Use when an agent must tell if interest in a topic is rising or fading: Wikipedia, Hacker News and news over time. For the articles use news_search. Returns daily topic trends for 1 to 5 topics: Wikipedia page views per language, Hacker News counts, news mentions, with 7- and 28-day change, spike days, direction and share. Not Google Trends data. Public attention signals, not Google Trends or search volume. `related`: related Wikipedia topics ranked by page views; not search queries and not Google Trends data. Topic = top English Wikipedia hit (see `article.title`). Complete days to yesterday; news only for the last 14 days. Cached 6 h. Bad input is a 422 and an upstream failure a 503 or 504 (not charged). Typically 3 to 15 s (first call for a topic; cached for 6 hours). Price: USD 0.005. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/topic-trends-attention-api.html

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• get_topic_trends_attentionA

  TanodA

  utilpeek: Use when an agent must tell if interest in a topic is rising or fading: Wikipedia, Hacker News and news over time. For the articles use news_search. Returns daily topic trends for 1 to 5 topics: Wikipedia page views per language, Hacker News counts, news mentions, with 7- and 28-day change, spike days, direction and share. Not Google Trends data. Public attention signals, not Google Trends or search volume. `related`: related Wikipedia topics ranked by page views; not search queries and not Google Trends data. Topic = top English Wikipedia hit (see `article.title`). Complete days to yesterday; news only for the last 14 days. Cached 6 h. Bad input is a 422 and an upstream failure a 503 or 504 (not charged). Typically 3 to 15 s (first call for a topic; cached for 6 hours). Price: USD 0.005. Free: 10 utilpeek calls per IP per UTC day. Docs: https://tanod.dev/learn/topic-trends-attention-api.html

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• web_searchA

  teardropA

  Real-time web search via Tavily for current events, fact-checking, and research. Use search_depth='advanced' for complex queries (higher quality, higher cost) and topic='news' or 'finance' for headlines or market information. Use when: Choose when the task needs current, external, or factual information not available from on-chain or local data — e.g. news, prices, documentation, or fact-checking. Limitations: Returns web snippets, not raw page bodies; results depend on Tavily coverage. Advanced depth costs more. Not a substitute for on-chain tools like get_token_price. Alternatives: get_token_price, http_fetch

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• address_related_addressesA

  NansenA

  Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.

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• address_related_addressesA

  NansenA

  Get information about related addresses of an input address. Note: This only includes the the "special" connections 'First Funder', 'Signer', 'Previous Signer', 'Multisig Signer of', 'Previous Multisig Signer of', 'Deployed via', 'Deployed by', 'Deployed Contract', 'Created Contract', 'Created by'. To get related wallets, also check address counterparties. First funder exchange withdrawal address does usually NOT belong to the same entity as the address, only deposit addresses. Only information is that it has been funded by the exchange.

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• get_ticketA

  Sweeppea MCPA

  Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). # get_ticket ## When to use Get full details of a support ticket by case number. Use fetch_open_tickets or fetch_closed_tickets first to find tickets, then use this tool with the case number to get complete information including notes, files, collaborators, and statistics. Present only human-readable information (case number, subject, dates, notes). ## Parameters to validate before calling - case_number (string, required) — The ticket case number (e.g., "HYXTNJV")

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• resolve-library-idA

  xpay✦ DevTools CollectionC

  Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'query-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.

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• crossload_topic_statsA

  CrossloadA

  Returns counts only, never the items themselves: for the items use crossload_search. Given one topic name, it counts the whole topic server-side and reports how it is distributed over bible books and chapters, authors, series, years, licences, categories, media and duration, plus the topics that co-occur on the same items. Use it to answer 'which passages/authors/years does this topic span', to find related topics by evidence rather than by guess, and before writing anything that sums up a topic: counting by hand means paging through every hit. The topic name must match crossload_browse exactly; a guessed spelling is rejected and nothing is counted. 'withoutBibleRef' says how many items carry no passage at all, which is the caveat to every statement about the passages of a topic. Each distribution lists the most frequent entries only, and says so when it leaves something out.

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• resolve-library-idA

  Context7A

  Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'Query Documentation' tool to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Each result includes: - Library ID: Context7-compatible identifier (format: /org/project) - Name: Library or package name - Description: Short summary - Code Snippets: Number of available code examples - Source Reputation: Authority indicator (High, Medium, Low, or Unknown) - Benchmark Score: Quality indicator (100 is the highest score) - Versions: List of versions if available. Use one of those versions if the user provides a version in their query. The format of the version is /org/project/version. For best results, select libraries based on name match, source reputation, snippet coverage, benchmark score, and relevance to your use case. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.

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• refresh_patent_familyA

  LightbringerA

  Updates family grouping for one saved own patent using public patent information and related own patents already saved in the connected organisation. Useful for an apparent missing relationship, an unfinished family update, or a requested check against newer public information. Imports already attempt family grouping automatically; routine refresh after import is unnecessary. Related publications must be imported separately before they can be included. Repeating import_patent only retrieves its saved outcome. Existing family relationships are retained, so refresh cannot remove incorrect relationships. Patent text, PDFs, images, legal status and own/competitor classification are unchanged. Competitor records are not supported. Returns counts of added and existing relationships, unresolved or ambiguous matches, and failures, with warnings. It does not return a list of family members. A null links result means the update did not finish; repeating the refresh attempts it again. Results do not establish complete family coverage.

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• firma_profilA

  Overfit — German tenders & procurement lawA

  Register profile of one German company from the Overfit dataset: name, address, legal form, register court, WZ industry code, website and alive verdict (active/dead/unknown) with the date of the status (as_of). No account or key required. Example user questions: 'Wer steckt hinter HRB 52093, Amtsgericht Hamburg?' (key K1101_HRB52093), 'Welche Adresse hat K1101_HRB52093?', 'Is this company still active?' Related: firma_status checks up to 25 companies at once; firma_finanzen returns the financial history; vergabe_suche finds tenders by topic.

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