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306,526 tools. Last updated 2026-07-27 01:32

"A tool for finding people on LinkedIn" matching MCP tools:

  • Company research for AI agents $0.03: web + scrape + firmographics + domain trust in one call. Alias: /api/x402/company-research. Ed25519-attested. Use before/after people enrichment. Upsell verified company file $0.95. ?q=company or domain. [x402 paid: GET /api/x402/deep-research-json price $0.03 on Base]
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  • Research a person before outreach: returns a synthesized profile (current role, company, location, career history, education, LinkedIn/social URLs) plus a candidates array for disambiguating namesakes. Use to personalize a first touch or brief before a meeting. Does NOT return contact channels (email/phone/telegram) — use contacts.discover to add a reachable channel, or the LinkedIn URL from the result for a connection request.
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  • 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.
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  • Enrich existing contacts with their full LinkedIn profile data via the connected LinkedIn account (Unipile) — headline, location, current company & position, full experience, education and skills are scraped from each contact's profile URL and saved onto the contact (and merged into profile_data). Use after search_google_xray to flesh out lightly-saved leads. Each contact is a real LinkedIn profile view, so keep batches small; max 8 per call. Returns per-contact enrichment status.
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  • Identify what a LinkedIn URL points at before fetching it. Give any LinkedIn profile, company, or post URL (utm params, www/m subdomains, trailing slashes are fine); get back {type: person|company|post, id, handle, canonical_url}. For profile URLs, id is the stable person URN; for company URLs, id is the stable company URN; for post URLs, id is the activity URN extracted from the URL. For people, use the returned handle or id with linkedin_get_profile or linkedin_get_posts. For companies, use the returned HANDLE with linkedin_get_company or linkedin_get_posts; the company URN/id is a linkedin_search_people filter input, not a fetch identifier. Costs 2 credits. Skip this tool when you already have a slug, URN, or clean URL: linkedin_get_profile and linkedin_get_company accept those directly, so resolving first would waste 2 credits. Not for non-LinkedIn URLs; it returns INVALID_INPUT for those.
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  • Schedule multiple posts at once from CSV content. USE THIS WHEN: • User has a spreadsheet or list of posts to schedule • Planning a content calendar for a month • Migrating content from another tool CSV FORMAT (required columns): • platform: linkedin, instagram, x, tiktok, threads • scheduled_time: ISO 8601 format (e.g., 2024-02-15T10:00:00Z) • text: Post content/caption OPTIONAL COLUMNS: • media_url: Image or video URL • first_comment: First comment to add (Instagram/LinkedIn) • hashtags: Additional hashtags to append PROCESS: 1. First call with validate_only: true to check for errors 2. Review validation report with user 3. Call again with validate_only: false to execute import
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  • LinkedIn data for AI agents: search, profiles, companies, posts. Free key, self-minted, no signup.

  • LinkedIn data for AI agents: structured profiles, people search, companies, and posts over MCP or REST. 500 free credits, no card.

  • Create a LinkedIn post on behalf of a connected profile. By default the post is saved as a 'draft' in the LinkedIn Posts page so the user can review/edit it before publishing. Set auto_publish=true to publish immediately — that path still respects the user's MCP human-in-the-loop setting (when approval is required, the post stays as a draft and the user must publish it from the LinkedIn Posts page in the app). A random 30–180 s anti-detection delay is applied before the publish call. Attachments are not supported via MCP — add images in the in-app post editor.
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  • Check whether the user's LinkedIn account is connected and active. Returns the connection state and, if not connected/blocked, how to fix it. Call this first when another tool returns code ACCOUNT_NOT_CONNECTED or ACCOUNT_BLOCKED. profile_id is optional — defaults to the active/first profile.
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  • Strips the background from a video frame-by-frame using rembg (u2netp) on AetherWave's Python service. Pass a public `videoUrl`. Choose `bgType: "transparent"` for an alpha-channel WebM output (compositing) or `bgType: "color"` with a `customColor` hex for a solid replacement. 2 credits per second. Slowest tool in the surface (per-frame processing); a 6s clip takes ~4 min, a 30s clip ~15-20 min. Works best on subjects with clear edges (people, products). Returns the processed video URL (R2-hosted).
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  • Profiles I've vouched in (people who used one of my invite codes). Returns handle + display_name + avatar_url + when they joined. Symmetric counterpart of `list_my_invite_codes` — that one is keyed on the codes I issued, this one is keyed on the humans who actually showed up.
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  • Get aggregate dining statistics for a neighborhood, city, or region: cuisine breakdown, grade distribution, price range, top neighborhoods, and highlighted restaurants. Use this for area-level context ("what is the dining scene like in Shoreditch?"), NOT for finding a specific restaurant or getting a personal recommendation. For "find me a quiet Italian near Shoreditch", use the recommend tool instead.
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  • Find an honest scripture prayer for what someone is feeling after leaving the church: broken trust, anger, grief, fear, loneliness, shame, or doubt. Give a feeling in their own words (e.g. "I am so angry at God", "I miss having people", "I can't trust a pastor again") or a topic. Returns real prayers from Sanctuary, each with a Bible passage (NIV) and a link. For people who left the church but did not leave Jesus. Not counseling; anyone in crisis is pointed to 988.
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  • Head-to-head verdict for two games on needmoar.games: which one players like more, each game's community score (the share of a library it beats) and rank, and the full distribution of opinions on both. Use this to answer "do people prefer A or B?". Pass Steam appids — resolve names with search_games first. The response links to the matching /vs page you can cite.
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  • Backfill Chats Pull all existing LinkedIn conversations from Unipile into Iridium. Run this once after connecting LinkedIn to populate your inbox. Also repairs any blank participant names from a previous backfill. Takes 10-30 seconds depending on conversation count. ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json
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  • Run Company Commenting Pipeline Generate LinkedIn comment drafts on recent posts by people who work at specific companies. USE THIS when the user names one or more companies: "comment on posts from people at Stripe" "I want to be visible to Anthropic and OpenAI engineers" "engage with anything Ramp employees posted this week" DO NOT USE for topic- or keyword-based commenting ("find me AI posts to comment on", "comment on posts about RAG") — use run_commenting_pipeline instead, which searches the whole of LinkedIn by the user's saved comment_topics. How it differs: this tool searches by AUTHOR COMPANY and recency only, with no keyword filter — ANDing a keyword against a single employer usually returns nothing on LinkedIn. Topical relevance is then judged by the model over whatever those employees actually posted. As a result it may legitimately return zero drafts if nobody at those companies posted anything worth engaging with; that is a valid outcome, not an error, and you should say so plainly rather than retrying. Takes up to 5 companies per call. Companies with no matching LinkedIn page come back in unresolved_companies — tell the user which ones failed. After calling this tool, ALWAYS display every draft to the user in full: **Post by [author_name] ([company]):** [post_text — display the complete text, never truncate] **Drafted comment:** [comment_text — display in full] Do NOT call approve_comment_draft or schedule anything unless the user explicitly asks. Args: companies: Company names, e.g. ["Stripe", "Ramp"]. Max 5. n: How many drafts to generate (default 3, capped by the user's remaining daily cap). date_posted: "past_24h" | "past_week" | "past_month". Default "past_week". ### Responses: **200**: Successful Response (Success Response) Content-Type: application/json
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  • Structured LinkedIn Ad Library search by company name, keyword, or companyId — use for a targeted B2B pull; use research_ads for open-ended research. Returns compact JSON {advertiser, headline, description, cta, link, media, dates, impressions} per ad — LinkedIn is the one library exposing real impression counts. Spends ScrapeCreators credits (~1).
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  • 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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  • 🔗 Link a new channel identity (email, phone, LinkedIn, etc.) to an existing contact. When to use: - User learns a contact's email or phone and wants to save it - User wants to link a LinkedIn/Instagram profile to an existing contact - Adding a second channel for an existing person Requires contact_id (entity_id) from contacts.find.
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  • Send a message to a thread, channel, or contact. Supports Telegram, Email, LinkedIn, and other connected channels. For LinkedIn posts (comment_thread kind), this posts a comment on the post. Can automatically resolve recipients and channels when not specified. Can send files/images/documents as attachments — pass `attachments=[file_id, ...]` with integer file IDs obtained from collections.list_files, search.files, or files.search. `text` is optional when attachments are provided.
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  • Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.
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