"People search for Swaroop Kallakuri" matching MCP tools:

• search_ens_names

  Name Whisper — ENS Intelligence Layer

  Search ENS names using natural language. Supports all query types: - Filtered search: "4-letter words under 0.1 ETH" - Concept search: "ocean themed names" (semantic similarity across 3.5M indexed ENS names) - Creative search: "names for a coffee brand" (AI-generated suggestions) - Collection search: "crypto terms expiring soon" - Activity: "what sold recently?" - Availability check: "is coffee.eth taken?" - Bulk check: "check apple.eth, banana.eth, cherry.eth" - Collection/club floor: "999 club floor", "cheapest 10k club names" (returns real listings sorted by price) Returns structured results with name, price, owner, tags, and availability info. It searches the NAME database by pattern/length/price/club/vibe — it does NOT know who real-world people, teams, brands, athletes, musicians, or films are. For "find me NBA players / pop stars / Pixar films / presidents" use enumerate_entities instead (it returns correctly-spelled labels). Use this for "floor of <club>" / "cheapest in <collection>" (find_alpha can't — it has no collection param). For lifecycle-window lists — "which names are in premium / Dutch auction", "names in grace period", "expiring soon" — use get_expiring_names instead: its grace/premium statuses are on-chain-validated and premium rows carry live pricing.

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• random_users

  Synthetic Data — Random User

  Generate synthetic / fake user profiles via the MwVideos random_users API (POST /mwvideos/api/random_users). Returns name, gender, location, email, login, dob, phone, picture, and nat. Use when the user asks for random users, fake people, sample contacts, demo personas, UI fixtures, or test profiles. Pass `results` for how many profiles (default 1, minimum 1) and `isPro` as 0 or 1 when relevant (default 0). Authenticated user_id is injected server-side — do not invent profiles; always call this tool. These are FAKE people for demos and testing, not real PII.

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• tip_split

  Moltline Personal Suite

  Compute the tip and per-person split for a bill. FREE. Typical input {"bill": 86.40, "tip_pct": 20, "people": 4} returns {"tip": 17.28, "total": 103.68, "per_person": 25.92}. Use for one bill shared among a group. Not for recurring household budgeting (budget_split). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "bill must be > 0 and people >= 1"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• tip_split

  Moltline Personal Suite

  Compute the tip and per-person split for a bill. FREE. Typical input {"bill": 86.40, "tip_pct": 20, "people": 4} returns {"tip": 17.28, "total": 103.68, "per_person": 25.92}. Use for one bill shared among a group. Not for recurring household budgeting (budget_split). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "bill must be > 0 and people >= 1"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• search_people

  noticed

  Search people by free text — name, company, keywords. Terms are OR-matched and ranked by how many match (more terms broadens, not narrows). To narrow: put AND between terms to require all (e.g. 'health AND medtech'), or prefix a term with + to require just it (e.g. '+rust berlin'). For roles/functions (founder, engineer, investor, …) use the `role` filter instead of free text — it catches title variants ('Founding Partner') that keywords miss, and free text over-matches bios/notes. To count people of a type, use structured filters and read `total` from the response — a free-text `total` counts keyword matches, not people of that type. scope:'own' (default) / scope:'public' (beyond your network + warm-intro paths). Optional company, location, skills, tags filters. A structured-filter zero = thin data, not absence — fall back to free text. Misspelled names/companies fall back to fuzzy matching (`fuzzy: true` = closest matches — confirm before trusting); concept queries with zero literal hits fall back to embedding similarity (`semantic: true` = related people, not literal matches). Free-text responses also return `strong_total` (rows matching ALL terms — the honest count) and per-row `matched_on` (which fields matched). In scope:'public', role/location/company/skills are applied to the global hits (`filtered: true`); filters that cannot apply there are listed in `unsupported_filters`.

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• web_search_exa

  exa

  Search the web for any topic and get clean, ready-to-use content. Best for: Finding current information, news, facts, people, companies, or answering questions about any topic. Returns: Clean text content from top search results. Query tips: describe the ideal page, not keywords. "blog post comparing React and Vue performance" not "React vs Vue". Use category:people / category:company to search through Linkedin profiles / companies respectively. If highlights are insufficient, follow up with web_fetch_exa on the best URLs.

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• osint_investigate_username_start

  dns

  Start an async OSINT investigation for a username (cross-platform presence, breach correlation). Owner/enterprise tier only — people-centric OSINT is restricted to prevent misuse. Returns an investigationId immediately — poll with osint_investigation_status and retrieve results with osint_investigation_report.

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• get_contact

  local-mcp

  Gets a contact from the Mac's Contacts app (Contacts.app) by name or ID. Pass `name` to look up directly by name (no need to search_contacts first — if several people match it returns a compact list to choose from), or `contact_id` for an exact lookup. For Microsoft 365 use m365_get_contact instead.

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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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• getPeopleById

  CoinPaprika

  Get the profile of one person in crypto by id: name, description or bio, teams_count, positions held, and linked projects plus social and code accounts. Use for 'who is Vitalik Buterin', 'background on Charlie Lee', 'projects tied to this founder'. Find the personId first with search or resolveId using type 'people'; this tool needs the exact id, not a name. Read-only. Params: personId (required) is a canonical person id such as 'vitalik-buterin' or 'satoshi-nakamoto'; an unknown id returns not found. No API key required.

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• search_community

  navmds

  Search patient discussion threads on community.navmds.com — the NavMDs forum where people discuss recovery timelines, what procedures actually cost them, choosing a surgeon, and what to expect. Use this for lived-experience questions ('what is rhinoplasty recovery like', 'how much did people actually pay for a tummy tuck', 'how do I pick a surgeon') where the doctor-directory tools can't help. Returns thread titles, categories, excerpts and canonical URLs; pass an id to get_community_thread for the full discussion. Community posts are patient discussion, not medical advice.

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• add_expense

  xupersplit

  Record something one person paid for the group. Split equally by default; pass split_between for a subset, or shares for an uneven split. One expense covers one set of people — a receipt whose items aren't all shared by everyone is several expenses, so call this once per group of items that the same people share, rather than splitting the total equally. Works on simple, accountless splits. Secure splits (created by a signed-in user) are read-only here and must be opened in a browser.

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• translate_i18n_json

  netcafe-translate

  Translate an i18n JSON locale file, keeping the key structure identical and placeholders ({name}, {{count}}, %s, HTML tags) intact. Pass existing_json to translate only the keys that are missing from it — the incremental sync people usually hand-roll a script for.

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• get_case_study

  LeanScale GTM Knowledge

  Retrieve LeanScale customer case studies — the problem the company had and how it was solved. Public keys get an anonymized version: customer names, people, third parties and delivery provenance are stripped, and outcomes/metrics/quotes are withheld. Internal keys get the full named studies. Search by sector, the kind of engagement, or the systems involved — searching by customer name will not work on a public key, because the names are not in the public corpus.

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• get_entity

  VineVerse — Bible Knowledge Graph

  Fetch any document in the knowledge base by path: People — people/moses Places — places/bethel-1 Themes — themes/faith Lexemes — lexemes/h430 Commandments — commandments/001-know-there-is-a-g-d Events — sa/events/exodus-from-egypt Epochs — epochs/david-reigns-in-jerusalem Books — bible/john Chapters — bible/john/3 Sources — sources/bibledata Datasets — references/cross-references Schema — schema/relations Returns the document's structured fields, its typed relations in BOTH directions, what links to it, and optionally its full text. The inbound direction matters: the corpus stores each relation once, on one side only, so a person's parents live on their parents' documents and only relations.inbound recovers them. Set body=true for the prose — a lexeme's Strong's definition, a commandment's Hebrew, a theme's outline of verses all live in the body and nowhere else. Where several people or places share a name the path is disambiguated with a number (people/zechariah-14, places/bethel-1) — search_concepts first if unsure. A collection path like "themes" or "sources" returns what that collection holds.

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• search_concepts

  VineVerse — Bible Knowledge Graph

  Search every document in the knowledge base by name, description, tag, path and type — people, places, themes, lexemes, commandments, events, epochs, books, chapters, sources and datasets alike. All terms must match (AND), so adding a word narrows the result. Use `type` to stop one collection swamping another: there are ~3,000 people AND ~3,000 themes, so an unfiltered name query is a coin flip between a person and a topic. This searches concept METADATA. To find a phrase in the text of Scripture use search_scripture; to fetch a reference you already know use get_passage.

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• osint_investigate_email_start

  dns

  Start an async OSINT investigation for an email address (breach exposure, account correlation). Owner/enterprise tier only — people-centric OSINT is restricted to prevent misuse. Returns an investigationId immediately — poll with osint_investigation_status and retrieve results with osint_investigation_report.

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• xpay_discover

  xpay✦ MCP Server

  Search XPay Hub for paid API services. Use this PROACTIVELY when the user asks you to: search the web, find emails, enrich contacts/companies, verify emails, find similar websites, extract web page content, get company news, search for people by title/company, get job postings, generate images, or any data lookup task. Returns matching servers with slugs, tool counts, and pricing. Use xpay_details next to see the full tool list for a server.

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• crypto_trending

  LiveDataLink

  See what's trending and hot in cryptocurrency right now. Returns the top trending coins on CoinGecko based on search activity and interest. Use this for 'what's trending in crypto?', 'hot cryptocurrencies', 'trending coins', 'what crypto is popular right now?', 'crypto buzz', 'what tokens are people looking at?', or any question about current crypto market interest and momentum.

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• tengu_v3_private_markets_search

  tengu-firm

  Search PRIVATE companies / investors (VC/PE) / funds / people / limited partners by name (prefix, case-insensitive), ticker, or CIK — relevance-ranked so the prominent entity is #1 (brand/AKA/former-name aware: 'Nubank'→Nu Holdings, 'Square'→Block). Use this FIRST for any private-company question (e.g. 'tell me about Stripe', 'who is Sequoia') to resolve the entity id, then call the company/dossier/realtime tools. type=all searches every entity kind. detail=full returns every column per hit (for rich tables).

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