Skip to main content
Glama
766,091 tools. Updated 2026-11-11 09:34

"A guide for finding LinkedIn profiles by name" matching MCP tools:

  • Returns the plain-language meaning and step-by-step fix for one Malinois finding. Use it while helping the user fix an issue reported by scan_app, or when they ask what a finding means. Pass the rule_id exactly as scan_app returned it. Read-only, no network, instant.
    ConnectorNo auth
  • Change a play's own knobs — name, goal, voice — its two switches, and the ACCOUNT's pacing (daily action cap, working hours: shared by every play on the LinkedIn account, not per play). `paused: true` is the human's stop button (nothing runs, nothing is sent); `paused: false` resumes. `status: 'active'` LAUNCHES a draft; `status: 'draft'` takes it back off the road. Seat rule, enforced server-side: no live LinkedIn seat ⇒ neither launch nor resume makes it active — it stays/falls back to draft and the note says so. Launching or resuming kicks the play's due lanes at once. Only launch, resume or raise the cap when the human asked for it in this conversation: an active play spends the month's lead quota and acts from the human's own LinkedIn account. Does not touch the autonomy ladder.
    Connector
    Destructive
    No auth
  • Rahul's contact details (email, LinkedIn, portfolio), with an option to email him directly. Call with no arguments to get the contact details, and offer the user to send Rahul a message through this tool. Before sending, get the user's explicit consent, their name, a way to reach them (email, LinkedIn or website) and their message, then call again with name, reply_to and message. Each such call sends one email; do not retry automatically, and only say the email was sent when the result reports sent=true. Args: name: The sender's name. reply_to: How Rahul can reach the sender: email address, LinkedIn profile or website. message: The message or introduction to send Rahul.
    ConnectorNo auth
  • What to know before going to a venue, from UPFRONT's venue guide: the bag policy and what fans can bring, gates and entry, parking, getting there by transit or rideshare, and food and drink, including what the guide says about gluten-free, vegetarian or vegan, halal and kosher food. Matches questions like "what's the bag policy at MetLife?", "when do doors open at MSG?" or "where do I park for Lambeau?". venue: the venue's name ("MetLife Stadium", "MSG", "Lumen Field, Seattle"). topics: optional; only these parts, at more length: any of "bags", "entry", "parking", "transit", "food". Without it, every part. Covers the 36 stadiums, arenas and amphitheaters UPFRONT has a guide for; for any other venue the answer says so and lists the ones with guides. Each part is the guide's own words ("text", plus the guide's "faq" questions and answers). factsLastChecked is the date UPFRONT last checked the guide's facts; rules can change for a single event, and the venue's own event page has the final word. guideUrl is the guide on UPFRONT. dietary lists what the guide says per diet; dietaryNotMentioned lists diets the guide doesn't cover, which doesn't mean the venue has nothing for them.
    ConnectorNo auth
  • Lusha: looks up 1-25 known business contacts by Lusha ID, name+company, LinkedIn URL, or email. Use when: fetching contact details for known specific contacts (single or batch up to 25). Do not use when: searching for contacts by criteria (prospecting_contact_search covers that case). Optional 'enrich' bundles reveal data; pass false for preview-only, then prospecting_contact_enrich with each result's `canReveal[].field`. Returns: Lusha contact profiles with available business data. Credits: consumes 1 Lusha credit per successful lookup; adds reveal credits when enrich is true (default). Rate limits apply per Lusha plan (per-minute/hourly/daily); exceeding returns HTTP 429. The account_usage tool returns current limits and remaining headroom.
    ConnectorOAuth
  • Find people on LinkedIn by keywords and filters. The right tool when you have a name, role, or 'who is the X at Y' question without a profile URL. Pass keywords (free text: name, title, or both) and any of first_name, last_name, title, school, current_company, past_company. If keywords is omitted it is derived from the name or title filters; school or company filters alone are rejected with INVALID_INPUT, so include keywords with those. Company filters accept a company name, slug, numeric id, or URN. current_company names are matched by LinkedIn's own company search: typo-tolerant and fuzzy, so results can include people whose headline merely mentions the company; when you need exact filtering, pass the numeric id or URN (linkedin_get_company returns both). past_company names are resolved to an id for you. Costs 10 credits including the first 10 results; each further 10 results add 1 credit (limit max 30; trial keys max 10). A cursor page is a NEW call priced the same way by its own limit, so one limit=30 call is much cheaper than three limit=10 pages; prefer a larger limit over paginating. Do NOT combine a past_company NAME or a company-URL filter with limit=30: resolving those spends one of the call's three internal fetches, so that combination is rejected; keep limit<=20 with them or pass the numeric id. current_company names never spend a fetch, so they combine with any limit. Returns name, position, location, urn, public_identifier per result, a cursor for the next page, and total_matches. Results with is_anonymous=true are private profiles; do not pass them to linkedin_get_profile. For one known person with a URL/slug, call linkedin_get_profile directly instead.
    ConnectorOAuth

Matching MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Multilingual name romanization lookup across Chinese, Japanese, Korean, Arabic, Vietnamese, and more. Resolves whether two name spellings refer to the same person — Chan/Chen/陳/陈, Hsu/Xu, Chou/Zhou — across Pinyin, Wade-Giles, Cantonese, Hokkien, and other romanization systems.
    MIT

Matching MCP Connectors

  • LinkedIn: The LinkedIn Data API offers access to detailed information on individuals, companies.

  • LinkedIn data for AI agents: search, profiles, companies, posts. Free key, self-minted, no signup.

  • Get USER PROFILES of people who interacted with an Instagram post. Returns full user data (bio, followerCount, followingCount, etc.). RETURNS USER PROFILES: id, username, fullName, biography, followerCount, followingCount, isVerified, profilePicUrl. Use for analyzing WHO engaged with a post. NOT FOR COMMENT TEXT: To read the actual comment content (what people wrote), use getInstagramCommentsByPostId instead. INTERACTION TYPES: "commenters" (users who commented), "likers" (users who liked). WHEN TO USE THIS TOOL: Analyzing commenters/likers demographics, finding influencers who engaged, building audience profiles, network analysis of who interacts with posts. WHEN TO USE getInstagramCommentsByPostId: Reading comment text, sentiment analysis of what was said, analyzing discussion content. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Auto API fallback for commenters when stale. PAGING (responseType="paging"): Async paginated results (1000 users per page with default fields), returns operationId - IMMEDIATELY call checkOperationStatus to get results. CSV export included via dataDumpExportOperationId. Supports pageNumber/tableName for subsequent pages. Optional fields (default: ["id", "username", "fullName"]). Available: biography, isPrivate, isVerified, followerCount, followingCount, mediaCount, profilePicUrl. This is a safe, read-only tool for analyzing searchable information.
    ConnectorOAuth
  • Send a one-off LinkedIn message to a specific saved contact outside any campaign. Prefer contact_id; Salesbot resolves the LinkedIn identifier internally. A random delay, daily/hourly limits, allowed hours and human approval are enforced. Refused with code DUPLICATE_RECENT_MESSAGE (and the campaign name + date) when a campaign of this LinkedIn account already messaged the person within the last 30 days; to continue an existing conversation use reply_to_chat.
    ConnectorAPI key
  • Create new guides Create one or more new guides based on provided queries. Each guide targets exactly ONE engine and ONE analysis mode, chosen with the optional `source` field (default `google`). How to request each guide type: 1. Google SERP guide (1 credit per guide): omit `source`, or pass `source: "google"`. Example payload: {"queries": ["best crm"], "lang": "en-us"} 1bis. Google AI Overview guide (1 credit per guide). Two modes, like AI engines: `source: "google_ai_overview"` builds the guide from the TEXT of Google's AI answers (AI Overview, completed with AI Mode answers) ; `source: "google_ai_overview_citations"` builds it from the content of the web SOURCES those answers cite (recommended for GEO). Same language/country parameters as a Google SERP guide, 1 credit per guide in both modes. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "google_ai_overview_citations"} 2. LLM ANSWER guide (4 credits per guide): pass the engine name alone, e.g. `source: "chatgpt"`. The guide is built from the answer text the AI generates for the query. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt"} 3. LLM CITATIONS guide (4 credits per guide) [RECOMMENDED AI mode]: pass the engine name with the `_citations` suffix, e.g. `source: "chatgpt_citations"`. The guide is built from the content of the web pages the AI cites in its answer. Example payload: {"queries": ["best crm"], "lang": "en-us", "source": "chatgpt_citations"} Which AI mode to pick? For GEO (getting a page visible in AI answers), prefer `<engine>_citations`: AI engines send traffic by CITING pages as sources, so the winning move is to look like the pages they cite. The answer-text mode (`<engine>` alone) is mostly useful to analyze how the AI phrases its own answer. When in doubt, pick `<engine>_citations`. The same two modes exist for every AI engine (chatgpt, perplexity, claude, gemini, grok, mistral, deepseek). To optimize the same page for several engines or modes (e.g. Google AND ChatGPT answers AND ChatGPT sources), create one guide per source value on the same query. IMPORTANT, HOW TO READ THE RESPONSE OF THIS ENDPOINT, WHICH SPENDS CREDITS. Queries listed in `guidesFailed` are PROVEN not to have produced a guide and their credit was given back (unless the account has unlimited credits, where nothing was reserved): re-sending them is free and correct. Queries listed in `guidesUnknown` have an UNDECIDABLE outcome and their credit is deliberately KEPT, because the guide was most likely written: DO NOT re-send them, you would pay for the same guide twice. Look them up in `GET /api/v1/guides` after a few minutes instead, and contact support if nothing shows up. Finally, a `200` is NOT a promise that every query produced a guide: compare `guides.length` with the number of queries you sent, never read `success` alone, and never re-send a query just because it is missing from `guides`.
    ConnectorNo auth
  • Retrieve the name and description of the company behind a job, by job ID. Use this tool when users want more detail about a company than search_jobs/get_job_details return (e.g. after finding a job and wanting to know more about the company that posted it). DO NOT use for: searching/discovering companies or jobs by keyword (use search_jobs instead). LLM USAGE INSTRUCTIONS: - The typical flow is: search_jobs to find jobs -> user picks one -> offer to look up the company -> if the user agrees, call this tool with that same job_id (the same guid used for get_job_details; no separate lookup step is needed first). - Do not call this automatically right after get_job_details; ask the user first unless they already explicitly asked for company details. - Not every company has published an employer branding profile, and some profiles are marked not visible by the company. When that happens this tool does NOT raise — it returns a Company with `message` set and empty name/desc. Relay that message to the user (e.g. "Looks like that company hasn't published a public profile.") rather than treating it as an error. Args: job_id: The unique identifier of the job whose company to look up (required). This must be the job's `guid` field from a search_jobs result, NOT its `id` field — `id` is a different, internal identifier that this tool does not accept. Returns: Company: Contains: - name: The company name (empty if message is set) - desc: The company description (empty if message is set) - message: Set instead of name/desc when there is no visible company profile to show; None when name/desc are populated Raises: Exception: If the job is not found, an API call itself fails (network error, non-200 response, GraphQL errors), or input validation errors occur
    ConnectorNo auth
  • 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.
    ConnectorOAuth
  • The user's LinkedIn post analytics from LinkedIn's own API (their connected account) — impressions, unique members reached, reactions, comments, and reshares. Use for questions like 'how many impressions did I get last month?' or 'how are my posts performing?'. Omit dates for lifetime totals; pass start_date/end_date for a window; set daily=true (with one specific metric) for a per-day series to describe trends. Numbers are live from LinkedIn, unlike get_my_linkedin_posts whose per-post counts come from a periodic sync.
    ConnectorNo auth
  • <summary>Attach a file from the user's library to a queued outreach email or LinkedIn message. Only attach when the user explicitly asks to attach a specific file to someone's message — never on your own initiative. attachment_id comes from list_attachments. Name the queued message by exactly one handle: recipient_email for a queued email (pending or awaiting approval); target_provider_id for a queued LinkedIn message, or target_slug for one whose profile is still resolving — both as shown on the row in manage_linkedin_invite_queue's status read. On LinkedIn only messages carry files, not connection requests or InMails, and LinkedIn takes fewer file types than email; a file the channel can't send is refused with the reason. The same file can be attached to several messages.</summary> <returns> <description>The attached file's name and every file now on that message, shaped `{'attached': True, 'filename', 'attachments': [{'id', 'filename', 'mime_type', 'size'}]}`.</description> </returns>
    ConnectorOAuth
  • <summary>Fetch recent LinkedIn posts from one or more profiles using Apify. Use this when you need to check what someone has posted on LinkedIn recently, to personalize outreach off something they actually said. RECENCY WINDOW: only posts from the last `posted_within_days` days (default 7) are returned — this tool answers "what has this person posted lately", not "show me their post history". A profile with nothing in that window comes back with `profiles[<key>]['status'] == 'no_recent_posts'` and no posts for that profile — that is normal and does not mean the lookup failed. In that case, personalize off the prospect's role, company, or headline instead of forcing a stale post reference; do not widen `posted_within_days` just to find something to quote unless the user asked for older posts specifically. Only the profile's own original posts count as "recent activity" — reshares and quote-posts are excluded. ALWAYS BATCH: pass every profile URL in ONE call — batching is both cheaper and faster than one call per profile. Up to 1000 profiles per call; split a larger list across calls. Each returned post carries a `profile_input` field identifying which profile it came from (the matched input identifier). COST: 0.02 credits per unique scrapeable profile searched, PLUS 0.5 credits for each profile that actually has a post in the window. If the user has fewer credits than profiles, only the affordable first profiles are looked up and the rest are reported in `skipped_profiles_due_to_credits`. LARGE-BATCH COST GATE: because each profile can cost up to 0.52 credits, a call that would search more than 100 profiles is refused with a ModelRetry that states the exact credit cost, UNLESS `large_batch_approved=True` is passed. Set `large_batch_approved=True` ONLY after the user has seen the credit cost and agreed to it — in interactive chat, that means you told them the number and they said yes; in stored trigger code, ONLY if the user explicitly approved this recurring spend when the trigger was set up. Do not set it reflexively to silence the retry.</summary> <returns> <description>A dict with the following keys. - posts: list of {url, text, author, posted_at, days_ago, profile_input, reactions, comments} — newest-first per profile. `reactions`/`comments` are engagement COUNTS, not the people who engaged — use `fetch_post_engagers` for the actual list of people. - total: number of posts returned. - profiles: dict keyed by the normalized profile identifier (the same value as each of that profile's post's `profile_input`), each `{'status': 'ok'|'no_recent_posts'|'not_found'|'lookup_failed', 'posts': int}` — covers every attempted or cached profile. `not_found` means the actor could not resolve the target (renamed/private/deleted); `no_recent_posts` means it resolved but nothing fell in the window. `lookup_failed` means the lookup itself failed and was not charged, so it says nothing about whether they posted: tell the user the lookup failed and try again later. (Distinct from the top-level `unresolvable_profiles` list below, which is inputs rejected at URL classification and never sent to the actor.) - posted_within_days, profiles_lookup_count, credits_charged, and optionally skipped_profiles_due_to_credits / warning / unresolvable_profiles.</description> </returns>
    ConnectorOAuth
  • <summary>Add and/or remove watched people or company pages on an existing `list`-mode LinkedIn monitor, incrementally — you pass only the profiles that are changing, never the whole list. `add` and `remove` are LinkedIn profile URLs or vanity slugs for people, and full `linkedin.com/company/...` URLs for company pages (a bare company name or slug is read as a person's slug). A swap is one call (`add=[new]`, `remove=[old]`). Both forms of one profile (full URL and bare slug) collapse to a single watched member, so re-adding someone already watched is a no-op. The change takes effect on the monitor's next scheduled run. Use this instead of re-running `set_up_linkedin_monitoring` whenever the user just wants to tweak who's watched ("also watch Bob", "drop Alice"): setup would force re-passing every existing member, and since each watched profile is billed per run, one dropped URL on the re-echo silently stops watching that person. To change schedule, voice, or the action flags, use `set_up_linkedin_monitoring` (members are preserved there when omitted); adding the very first members or resetting the whole list is setup too.</summary> <returns> <description>Dict with status ('active'), name, members_count, added, removed, unresolved, and message.</description> </returns>
    ConnectorOAuth
  • Fetches one vertodigital.com page in full as clean markdown, without navigation or footer. Not a search: take the slug from vertodigital_search_site, or guess it, since an unknown slug returns an isError result with a hint. slug "contact" returns the contact page followed by a "Contact details" list: name and role, email, LinkedIn, reply time and form URL. Fetched live and returned whole, most pages under 20 KB, no pagination.
    ConnectorNo auth
  • Browse Signed's public angel investor directory: every investor with a public profile, most complete profiles first, optionally narrowed to one sector or one check size. Each result carries the investor's login (for get_investor), what they invest in, the checks they write, their pinned portfolio companies, and whether they take pitches. Use search_investors when you have a name or a keyword.
    ConnectorNo auth
  • Browse Signed's public angel investor directory: every investor with a public profile, most complete profiles first, optionally narrowed to one sector or one check size. Each result carries the investor's login (for get_investor), what they invest in, the checks they write, their pinned portfolio companies, and whether they take pitches. Use search_investors when you have a name or a keyword.
    ConnectorNo auth
  • PERMANENTLY delete saved contacts (LinkedIn profiles) including their campaign memberships, scheduled actions and campaign history. Contacts with pending or in-progress campaign steps are refused unless force: true (prefer exclude_contacts_from_campaign when the goal is just to stop outreach). Linked CRM leads are kept (unlinked) unless delete_crm_leads: true. Cannot be undone: only on the user's explicit request, show them the contacts first and pass confirm: true after they agree.
    Connector
    Destructive
    API key
  • Find physicist profiles in INSPIRE-HEP by name, INSPIRE BAI, ORCID, INSPIRE ID, or author recid. Each profile carries its recid, BAI, ORCID, current and past positions, advisors, arXiv categories, links, awards, and a literatureQuery that selects the person's papers in cern_inspire_search_literature. A name returns ranked candidates (a bare surname can match tens of thousands of profiles, and a less-cited namesake can rank first), so confirm the person from positions and categories before passing a bai or recid to cern_inspire_get_citation_summary. An identifier matches exactly.
    ConnectorNo auth
  • One LinkedIn person: their about text, location, followers, companies and latest posts. Use when: checking a person before outreach, reading how they describe themselves. Not for: a company page, use linkedin_get_company. An email address, use leads_get_email. A person's full employment record, use leads_get_person. Returns: Record. The profile's handle, name, bio, followers, location, experience[] (the companies and schools on the profile, with dates but no titles), recent_posts[] (their latest posts, for linkedin_get_comments) and related (similar profiles), with other profile fields where LinkedIn fills them. null or empty = the platform does not publish it. Give one of url or handle (the part after /in/). Cost: 1 credit. Cached 24 hours. Example: {"url": "https://www.linkedin.com/in/williamhgates"}
    ConnectorAPI key