"A server for exploring cryptocurrency-related topics" matching MCP tools:

• cti_get_guidelinesA

  website-searchA

  Get Lenny Zeltser's expert CTI writing guidelines. Topics include tone, words, structure, executive_summary, voice, articles, summary, brief (one-page brief section guidance), handoffs (cross-server routing), methodology (the three subsections), fields (per-field guidance), and CTI-specific topics: attribution (full Six Signals prose), confidence (ICD-203 ladder), pyramid_of_pain, six_signals (signals table only), and anti_patterns. The general writing topics (tone/words/structure/executive_summary) now defer to `get_security_writing_guidelines` for the canonical Five Elements rules; CTI-specific content lives in the other topics. Pair the 'fields' topic with field_id for single-field guidance. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.

  ConnectorNo auth

• get_action_schemaA

  FlowCastleA

  Read-only, needs no API key. The action-authoring contract apply_actions batches are validated against. With NO arguments, returns an INDEX of block types, actions, action kinds and topics with descriptions and sizes. Then request only what this bot uses. Detail responses include essential rules and basic shapes; follow `seeWhen` before using specialized features: templateExpressions for computed CEL expressions, groupVariables for sender/chat variable ownership, triggerDetails for webhook/module events and trigger output mappings. Responses return whole sections within the budget. Request `omitted` names again; `alsoRelevant` lists referenced topics that did not fit.

  ConnectorNo auth

• get_leadproof_evaluation_pathA

  LeadProof Sales AgentA

  Use first when a buyer is exploring LeadProof without supplying workflow data. Returns the official no-purchase sequence: buyer guide, fictional example audit, browser-only calculator, public workflow tests, and no-card trial. For a scored workflow diagnosis, use audit_lead_workflow; for replay testing, use get_leadproof_replay_trial; for paid checkout, use get_leadproof_checkout only after authorization.

  ConnectorNo auth

• get_graphA

  Enhanciar: AI company brainA

  Return the workspace's knowledge graph: topics, links and clusters. Use it to draw or reason about the overall shape of the brain (which topics are hubs, how areas connect), not to read content: for page text use ``get_page``, for code dependencies use ``impact``. It takes no arguments and can be large. Returns ``{nodes, links, communities, stats}``, the graph the app's Map shows, with pages you cannot open left out, or ``{error}`` when no graph has been built for this workspace yet. It costs no AI tokens.

  ConnectorAPI key

• workspace_setupA

  ZeroWidthB

  Creates folders for the work someone described, rooms linked to those folders (with your first message in each, the concrete next step), and starting pieces filed inside. Records what the workspace is for, which orders what home offers first. Use it once you know what they're working on, typically near the end of setting a workspace up. Say in one sentence what you're setting up and call it; the approval card lists the folders, rooms, and pieces, so don't list them in words too. Every workspace already has a General room, so don't recreate it. May return needs_confirmation; then wait for approval. Starting shapes by kind of work (adapt, drop, or combine; name everything after what they told you): - process_redesign: a folder per process being changed ("Claims intake redesign"). Pieces: a Compass page with pageType WORKFLOW describing how it works today, a Napkin diagram of current and proposed steps, a doc for the change plan. Room: "<process> redesign". After setup, offer a Ledger expectation for the metric they want to move. - research: a folder per study or question. Pieces: a research-plan doc (question, who, method), a synthesis doc. Room: "<topic> research". After setup, offer a Prism field for the question. - ai_design: a folder per AI feature. Pieces: a spec doc (what it does, for whom, what good looks like), a Caliper dataset for test cases. Room: the feature's name, with the flow in it once one exists (file an existing flow via existing). After setup, offer to scaffold the flow and a rubric. - knowledge: a folder per domain. Pieces: Compass pages for the main topics. Room: "Ask about <domain>". - exploring: usually nothing beyond their priorities and the General room; offer one small first thing instead. Priorities are Compass pages made with compass_pages_create before this, not pieces here; name folders after the work that moves them.

  Connector

  Destructive

  No auth

• 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).

  ConnectorNo auth

• searchA

  ScrapeMole QuotesA

  Use to resolve a company or asset name to a tradable ticker before calling /v1/quote or /v1/history: "apple" -> AAPL, "s&p 500" -> ^GSPC, "volkswagen" -> VOW3.DE, "bitcoin" -> BTC-USD. Returns up to 20 matches with symbol, short/long name, quote type (EQUITY, ETF, INDEX, MUTUALFUND, CURRENCY, CRYPTOCURRENCY, FUTURE), exchange, sector and industry where known. Paid tool: x402 USDC on Base, $0.002 per successful call; call without payment to receive the PaymentRequired object.

  ConnectorNo auth

• get_planning_schema_indexA

  One U EducationA

  Discover unfamiliar fields. Omit index_ref for the root; each scope includes topic index_ref links, so follow the needed topic directly without first opening its scope. Follow returned references for nested records, dictionary values and variants. Collect the needed schema_ref values across topics, then read them together with get_planning_schema_definition. Reuse cached directories and definitions at the same schema name/version. Priorities are interview order, not requiredness. Do not explore unrelated topics or read schema before every save. Requires the authenticated One U Education account and declared planning scope; credentials and user identity are never tool arguments.

  ConnectorNo auth

• get_reading_trajectoryA

  ReadingRateA

  Returns written insights about how the reader's reading has been changing: genres they are exploring, authors they return to, their pace, and how their themes are shifting. When none are current, ReadingRate generates them first: it sends the reader's past year of finished books (titles, authors, genres, themes, ratings and dates) to its AI provider, stores the new insights in the reader's account for seven days in place of the earlier ones, and counts the generation against the reader's ReadingRate AI usage. Generating takes several seconds and happens at most once a day. Use when the reader asks about their reading patterns, trends or trajectory. For numbers such as pace and genres, use get_reading_stats.

  Connector

  Destructive

  No auth

• recommendA

  sellaA

  Ranked related listings with per-item reasons. Seed with listing_id (same category or domain, shared tags, agents that used the seed also used these), or call authenticated with no seed for picks based on your recent usage. Not a keyword search: use search_catalog for that.

  ConnectorNo auth

• get_nursing_diagnosisA

  CarePlanKit — Nursing Care PlansA

  Get care plan material for a specific NANDA-style nursing diagnosis: its definition, related factors (the "related to" clause), defining characteristics (the "as evidenced by" clause), SMART goals, interventions, and the conditions where it is a priority. Use when a nursing student asks about a diagnosis rather than a disease, for example "risk for infection", "acute pain", "impaired gas exchange", "ineffective coping" or "risk for falls", or asks how to write a three-part diagnosis or an AEB statement. Educational reference, not medical advice.

  ConnectorNo auth

• analyze_citation_structure_batchA

  VantageA

  Analyze the structural shape of the winning AI answer across several related keywords/topics in one call: does each lead with a list, how long is the opening, how many sources it cites. It covers a topic cluster in one call, for content planning across related pieces. Read-only: no side effects, safe to retry. Costs 1 quota unit per keyword in the batch (free tier is 30 units/month shared across all the metered tools, so up to 30 keywords total that period if nothing else is used). A per-keyword provider error doesn't fail the whole batch - that keyword's entry just carries an "error" field instead. Returns: {"results" (list, one {"keyword", ...same shape as analyze_citation_structure, or "error"} per keyword, in the order given), "summary": {"topics_analyzed", "topics_requested", "list_led_count", "avg_sources_cited", "avg_community_pct" (average source_mix.community_pct across the analyzed topics)}}. Args: keywords: topics/queries to analyze, e.g. ["how to reduce churn", "churn rate benchmarks", "reduce customer churn saas"]. Max 10. country: market to read the answers in, e.g. "Italy". Defaults to "United States". language: language code, e.g. "it". Defaults to "en". engine: "chat_gpt" (default), "gemini" or "perplexity", as in analyze_citation_structure. One answer per topic.

  ConnectorAPI key

• related_topicsA

  Web Search & Data APIA

  Google Trends related topics for a keyword. Returns the topics and named entities associated with a keyword in a given country, as top topics scored 0-100 relative to each other and rising topics with percentage growth, each carrying its entity type. Google Trends topic discovery for market research and content planning. [$0.03/call]. Params — keyword: the search term; geo: ISO-3166 alpha-2 country code, e.g. US, GB, DE, JP (213 countries supported); timeframe: time window. Each response reports its own bucket size in `granularity`: past_30_days and past_90_days return a daily series, past_12_months and past_5_years weekly, windows under a day hourly (2004_present|past_12_months|past_30_days|past_4_hours|past_5_years|past_7_days|past_90_days|past_day|past_hour) Example params: {'keyword': 'bitcoin', 'geo': 'US'}

  ConnectorNo auth

• linkedin_get_companyA

  Manifold MCPA

  One LinkedIn company page: headcount, industry, location and website. Use when: sizing a company from its own page, confirming an industry and a headcount band before outreach. Not for: a company's own posts, use linkedin_get_company_posts. A richer firmographic record, use leads_get_company. A person, use linkedin_get_profile. Returns: Record. The page's handle, name, bio, followers, website, location, industry, employees (LinkedIn's own headcount), founded, company_type, topics (its specialties), people (the few employees the page shows, with profile URLs and usually no title) and related (similar pages), with other profile fields where LinkedIn fills them. null or empty = the platform does not publish it. kind is company. running_ads and ad_library_id are always null here: the page does not say whether the company advertises; ads_get_advertiser_ads with platform linkedin and the company name does. Give one of handle (the company slug) or url. Cost: 1 credit. Cached 24 hours. Example: {"handle": "shopify"}

  ConnectorAPI key

• adidas_product_review_topicsA

  Crawlora MCPB

  Adidas review topics for one product model: the topics its customer reviews can be filtered by, commonly satisfaction, comfort, color, purchase, fit, appearance, quality and style. Feed a topics[].topic value back to adidas_product_reviews as its topic argument to read only the reviews about that aspect. The vocabulary is per model rather than a fixed list, so read it per model instead of hard-coding it. model_number is the Adidas model number (e.g. SAMBAU2312) from an adidas_search result's products[].model_number field -- NOT the SKU in products[].id. A model with no reviews, including an unrecognized model_number, returns an empty list rather than an error. The label field is a display form of topic, not translated text: Adidas returns the same English values for every locale here.

  ConnectorNo auth

• get_helpA

  CapableA

  Search verified Capable how-to/setup/troubleshooting articles verbatim with citations, or fetch article_key; offer related topics. Use get_context/search_records for CRM data. Answer ONLY from returned articles and cite titles; never guess product behavior from memory. If confident:false or no article answers, say no verified answer is available and offer hello@capable.run as backup. Call with diagnostics:true, before saying their mail or calendar isn't connected: both connect to Capable, apart from any connector this chat has. Checks saved connection/import/agent settings. Diagnostics show setup facts, not live provider health. When to use: Questions about how Capable itself works, or checking saved connection status. Example: How do I set up a booking link in Capable?

  ConnectorAPI key

• arbitrum_tx_eventsA

  Bitquery: Crypto & Blockchain DataA

  ALL EVENT LOGS in one or more Arbitrum (arb, ARB, Arbitrum One, L2) transactions, including decoded arguments plus raw topics and data. Use after arbitrum_find_events identifies a transaction, or whenever a tx receipt's complete event payload is needed. NOT the value moved in a transaction → use arbitrum_tx_transfers. Accepts up to 50 transaction hashes separated by "|". `Parsed=0` means no ABI matched: Event, Signature and Arguments can then be empty, but Topic0, Topics and Data remain usable. Arguments are named objects with Name, Type, string Value and Indexed; integer values are decimal strings to avoid JSON-number precision loss; use raw Topics/Data for values wider than the decoded numeric fields. Argument names come from one known ABI for the Signature and can be another contract's wording - Topics and Data are the log's own. By default, reverted call-frame logs, removed logs and failed transactions are excluded; set only_successful=0 to inspect them with the returned status fields.

  ConnectorOAuth

• arc_tx_eventsA

  Bitquery: Crypto & Blockchain DataA

  ALL EVENT LOGS in one or more Arc (arc, ARC, Arc Network, Circle Arc) transactions, including decoded arguments plus raw topics and data. Use after arc_find_events identifies a transaction, or whenever a tx receipt's complete event payload is needed. Accepts up to 50 transaction hashes separated by "|". `Parsed=0` means no ABI matched: Event, Signature and Arguments can then be empty, but Topic0, Topics and Data remain usable. NOT the value moved in a transaction → use arc_tx_transfers. Arguments are named objects with Name, Type, string Value and Indexed; integer values are decimal strings to avoid JSON-number precision loss; use raw Topics/Data for values wider than the decoded numeric fields. Argument names come from one known ABI for the Signature and can be another contract's wording - Topics and Data are the log's own. By default, reverted call-frame logs, removed logs and failed transactions are excluded; set only_successful=0 to inspect them with the returned status fields.

  ConnectorOAuth

• base_tx_eventsA

  Bitquery: Crypto & Blockchain DataA

  ALL EVENT LOGS in one or more Base (base, L2, Coinbase L2) transactions, including decoded arguments plus raw topics and data. Use after base_find_events identifies a transaction, or whenever a tx receipt's complete event payload is needed. Accepts up to 50 transaction hashes separated by "|". `Parsed=0` means no ABI matched: Event, Signature and Arguments can then be empty, but Topic0, Topics and Data remain usable. NOT the value moved in a transaction → use base_tx_transfers. Arguments are named objects with Name, Type, string Value and Indexed; integer values are decimal strings to avoid JSON-number precision loss; use raw Topics/Data for values wider than the decoded numeric fields. Argument names come from one known ABI for the Signature and can be another contract's wording - Topics and Data are the log's own. By default, reverted call-frame logs, removed logs and failed transactions are excluded; set only_successful=0 to inspect them with the returned status fields.

  ConnectorOAuth

• eth_tx_eventsA

  Bitquery: Crypto & Blockchain DataA

  ALL EVENT LOGS in one or more Ethereum (eth, ETH, mainnet, L1) transactions, including decoded arguments plus raw topics and data. Use after eth_find_events identifies a transaction, or whenever a tx receipt's complete event payload is needed. Accepts up to 50 transaction hashes separated by "|". `Parsed=0` means no ABI matched: Event, Signature and Arguments can then be empty, but Topic0, Topics and Data remain usable. NOT the value moved in a transaction → use eth_tx_transfers. Arguments are named objects with Name, Type, string Value and Indexed; integer values are decimal strings to avoid JSON-number precision loss; use raw Topics/Data for values wider than the decoded numeric fields. Argument names come from one known ABI for the Signature and can be another contract's wording - Topics and Data are the log's own. By default, reverted call-frame logs, removed logs and failed transactions are excluded; set only_successful=0 to inspect them, using TxSuccess / CallSuccess / Reverted / Removed to interpret each row.

  ConnectorOAuth

• matic_tx_eventsA

  Bitquery: Crypto & Blockchain DataA

  ALL EVENT LOGS in one or more Polygon (matic, POL, MATIC, PoS) transactions, including decoded arguments plus raw topics and data. Use after matic_find_events identifies a transaction, or whenever a tx receipt's complete event payload is needed. NOT the value moved in a transaction → use matic_tx_transfers. Accepts up to 50 transaction hashes separated by "|". `Parsed=0` means no ABI matched: Event, Signature and Arguments can then be empty, but Topic0, Topics and Data remain usable. Arguments are named objects with Name, Type, string Value and Indexed; integer values are decimal strings to avoid JSON-number precision loss; use raw Topics/Data for values wider than the decoded numeric fields. Argument names come from one known ABI for the Signature and can be another contract's wording - Topics and Data are the log's own. By default, reverted call-frame logs, removed logs and failed transactions are excluded; set only_successful=0 to inspect them with the returned status fields.

  ConnectorOAuth