Revternal

Quality – / 100not scored

Not scored: fewer than three checks apply, usually because no remote answered a credential-free probe or the server is a local package.

Tool list unknown · tools last changed 2026-10-17 · checked 2026-10-20 09:31 UTC

Tool changes

• 2026-10-17revternal_enrich_personremoved

  Enrich one person from their LinkedIn profile URL via Revternal — full normalized profile: name, headline, current company and title, past work experience, education, skills, certifications, and profile picture. Default (fetch_live: false) reads Revternal's existing record for that URL and returns found:false if there is none; set fetch_live: true to have Revternal fetch a fresh profile (slower, higher vendor cost). Use to enrich a lead, candidate, or contact you already have a LinkedIn URL for. Requires your own Revternal API key (BYOK) — pass it as _apiKey. Example: revternal_enrich_person({ linkedin_url: "https://www.linkedin.com/in/vishalv06" }).

• 2026-10-17revternal_people_searchremoved

  Search Revternal's people-intelligence index for any person — not just developers — by job title, seniority, location, current company domain, or skills. Returns matching people with name, headline, and profile fields, plus an opaque cursor for the next page. At least one filter is required. Seniority values: entry, senior, manager, director, vp, c_suite, owner, partner. Countries use 3-letter ISO codes (USA, GBR, IND). Use for recruiting, GTM prospecting, and lead research when you have criteria rather than a specific profile. Requires your own Revternal API key (BYOK) — pass it as _apiKey. Example: revternal_people_search({ job_titles: ["Software Engineer"], countries: ["USA"], page_size: 5 }).

• 2026-10-17revternal_search_developersremoved

  Search a live index of ~12M developers (GitHub-centric) by role, primary programming language, location, and recent activity — for recruiting, GTM/lead-gen, and developer-audience research. Each result is a matching GitHub username, display name, and profile URL; your search filters are echoed back on the response rather than repeated per result. Filters: role (e.g. "backend engineer", "ai/ml engineer" — spaces/punctuation are normalized to underscores), primary_language (e.g. "Python", "Rust"), location, active_within_days (only devs active in the last N days), sort_by ("last_active", "commit_quality", or "external_contribution_ratio"; default last_active). Paginate with limit + offset — each requested slot costs 1 credit. Requires your own Revternal API key (BYOK) — pass it as _apiKey. Example: revternal_search_developers({ primary_language: "Rust", active_within_days: 30, limit: 20, _apiKey: "..." }).

• 2026-10-17recallremoved

  Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.

• 2026-10-17rememberremoved

  Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

• 2026-10-17scan_competitor_ai_presenceremoved

  Compare AI visibility across multiple entities side-by-side. Probes each entity (your brand + N competitors) with ai_visibility_check, ranks by score, surfaces which is most/least recognized. Useful for competitive AI-marketing audits: "does Claude know about us as well as our competitors?". Returns ranked list with score, confidence, signal density per entity.

• 2026-10-17ai_visibility_checkremoved

  Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass `_apiKey` to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.

• 2026-10-17suggest_questionsremoved

  What can I ask Pipeworx? / what is Pipeworx good for? / what can you do? / give me ideas / show me examples / getting started / what data do you have? — the onboarding entry point for an agent that just connected and wants to know what is worth asking. Returns category-bucketed example questions (company financials, drugs & clinical trials, economics, real estate, prediction markets, weather, government & patents, science & academia, news) — each with the exact tool + argument shape that answers it, drawn from the live catalog of thousands of tools. Call with no arguments for the full spread, or pass `topic` (e.g. "finance", "pharma", "betting") to focus. Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools (ask_pipeworx, entity_profile, compare_entities, etc.).

• 2026-10-17pipeworx_trendingremoved

  What other AI agents are calling on Pipeworx right now. Returns the top tools, top packs, and total call volume over a recent window (24h, 7d, or 30d). Useful for: (1) discovering what data sources are hot for current events, (2) confirming a popular tool is the canonical choice before asking your own question, (3) seeing whether your use case aligns with what most agents need. Self-aggregating signal — derived from CF analytics-engine, no PII, just (pack, tool, count). Cached 5min-1h depending on window.

• 2026-10-17archive_compareremoved

  What changed on a web page between two dates? Resolves the archived capture nearest each date (Internet Archive Wayback Machine, Common Crawl, Arquivo.pt), extracts the readable text of both, and returns a text-level diff: the segments removed and added, a similarity score, and both capture timestamps with their gaps from the requested dates. It diffs readable text, not markup, so a restyled page with the same words reads changed:false. Identical captures answer changed:false with the reason (same capture, identical content digest, or identical text), never an empty diff. A date with no capture within max_gap_days (default 30) answers status "no_capture", naming which side is missing and the nearest captures to it. Typical uses: how a privacy policy, terms of service, pricing or product page changed; whether a statement was on a page at a given time.

Showing the latest 10. Full history: list_changes · trend: quality JSON.

Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. How it is computed.