"Amazon S3" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Amazon competitor research for Claude, ChatGPT and other MCP clients: complaint topics from Amazon's "Customers say" summary of all reviews, 90-day price/rank/review history, listing audits (0-100), keyword rankings with who's advertising, and daily Competitor Watch emails when a rival's price, stock, rank or complaints change. 12 Amazon marketplaces, plus Shopify store teardowns and tracking. 25 free credits. Sample: zonrival.com/sample
Browse, upload, download, and share files in your S3-compatible buckets with delegated roles.
Amazon PPC data, P&L and inventory for AI assistants; changes only after your approval.
Amazon Seller Central, Ads and Vendor Central in ChatGPT & Claude. 106 tools; writes need approval.
Connects Amazon Seller Central and Amazon Advertising to any MCP client. Settlement-accurate P&L - every fee, refund and reimbursement as Amazon posted it - plus contribution margin and breakeven per product, per marketplace, per day. Full Sponsored Products, Brands and Display management: search terms, placements, keyword and competitor research, dayparting, automation rules. 110 tools: 78 read-only (P&L, PPC at every grain, SQP/Brand Analytics, inventory, forecasting), 28 staged writes (every change is a proposal you review and confirm before it reaches Amazon - there is no autonomous-write mode), and 4 confirmation & support tools. 4 additional AI Workforce tools appear once a client is connected.
Amazon Seller Central and Ads data for AI: account health, FBA inventory, reimbursements, keywords.
Audit TikTok Shop & Amazon affiliate scripts for policy violations via MCP.
Built for human creators. Register a timestamp on Polygon proving you made something, the moment you did. Your file is never uploaded, watermarked, or altered: only its cryptographic fingerprint ever reaches spArxx.io, zero-knowledge by design. A human still provisions the account behind the connection. This is deliberate, since this registration only means something with a human behind it.
Your agent needs the derived numbers — domain authority, what a site ranks for, related and relevant keywords, search intent, and who the real competitors are — for Google, Amazon and the app stores. **What you can ask for** • "What is this domain's authority, and how has its rank history moved?" • "Which keywords does this site rank for, and with what intent?" • "Who are this domain's organic competitors, and where do we overlap?" • "Which keywords does this Amazon product rank for?" • "Compare these two domains keyword by keyword." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-labs/mcp and sign in with OAuth — there is no key to create or paste. 46 tools: ranked, related and relevant keywords, keyword ideas and intent, domain authority and rank history, competitor and intersection analysis, bulk metrics, plus the same shapes for Amazon products and Apple and Google Play apps. **Why this rather than the source** Ahrefs domain rating, Semrush rank history and DataForSEO Labs answering the same questions side by side. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Size the competitor here, then ask the same agent for their traffic mix or their contacts — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo/mcp for all of it at once — rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
Your agent needs marketplace data — what a product costs on Amazon and Google Shopping, who the sellers are, what reviewers actually complain about. **What you can ask for** • "What is this ASIN's price history, rating and seller list?" • "Who else sells this product, and at what price?" • "Pull the reviews for this product and group the complaints." • "What comes up on Google Shopping for this query in the UK?" • "Compare these products across both marketplaces." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-merchant/mcp and sign in with OAuth — there is no key to create or paste. 22 tools: Amazon products, ASIN detail and sellers; Google Shopping products, product info, sellers and reviews; live and queued forms, with raw HTML where you need it. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Price the product here, then ask the same agent what the brand's site traffic or ad spend looks like — without adding a second server. **What it costs** Finding and inspecting an operation is free. Running one is billed per call at API prices, with no seat and no monthly minimum, and every call takes max_price_usd so an agent cannot overspend by accident. **Where else it reaches** https://mcp.aisa.one/seo/mcp for all of it at once — rankings, keywords, backlinks, site health and AI-answer visibility across DataForSEO, Semrush and Ahrefs.
Remote streamable-HTTP MCP server running on a single Cloudflare Worker. Your assistant gets live Airbnb, Amazon, Booking.com, Google Flights, Maps and Reddit data, social search on X, Instagram and TikTok, the Meta Ad Library, and image/video generation without any keys. Connect your own accounts to let it send WhatsApp or Telegram messages, work an IMAP inbox, manage Meta Ads campaigns and publish to X and LinkedIn. OAuth 2.1 with PKCE; stored credentials are AES-256-GCM encrypted.
# **RChilli MCP Hub** RChilli MCP Hub is a production-grade MCP server that exposes RChilli's full HR data intelligence platform as 17 AI-callable tools across 4 categories. Built on 15+ years of HR data intelligence, it is trusted by ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. Every tool is read-only and returns a consistent, structured JSON response — no raw exceptions, no inconsistent formats. <br> --- <br> # **Tools — 17 Total** userkey and subuserid are injected automatically from your Bearer token — you never need to pass them manually. <br> --- <br> # **🔍 Resume & Job Description Parsing — 3 tools** <br> > ### **`extract_resume_data`** > > Extracts and converts resumes, CVs, and candidate documents into structured, searchable profiles with contact details, skills, experience, education, certifications, and taxonomy-enriched data for ATS, HCM, and AI recruiting workflows. When used on a careers page or application form, the same extraction call auto-fills every application field in under 10 seconds — documented to increase candidate conversion by up to 194%. Supports 40+ languages with English-normalized output for global intake, and runs in batch mode to process legacy databases or migration backlogs overnight at scale. Also supports resume reprocessing — re-running previously extracted resumes through the latest extraction logic and taxonomy version to bring older records up to current data quality, without requiring a new document from the candidate. Distinct from bulk import (first-time extraction of a new batch) and from talent data refresh (re-enrichment from a newer submitted resume). <br> > ### **`extract_resume_data_from_url`** > > Accepts a direct URL to a PDF, DOCX, or RTF file and returns the same normalized JSON profile as the Resume Data Extraction tool. Ideal for pipeline automation where resumes are stored in cloud storage, S3, or email attachments. Also supports the same auto-fill, multilingual, and batch-processing capabilities as the core extraction tool for URL-based intake sources. <br> > ### **`extract_job_data`** > > Extracts and converts job descriptions into structured hiring data including job title, required skills, preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements for recruitment automation and candidate matching. <br> --- <br> # **🧠 Skills & Job Taxonomy — 4 tools** <br> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, all aliases, related skills, proficiency levels, and O*NET/ESCO mappings. Use when you need the complete record rather than a ranked search. <br> > ### **`lookup_job_profile`** > > Returns authoritative detail for a known job profile including canonical title, SOC/O*NET code, job family, typical required and preferred skills, salary bands, and work context. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string (min 2 chars) and returns up to 10 ranked autocomplete suggestions with canonical names and categories. Prevents free-text entry errors and keeps skill data clean at point of entry. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title string and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to taxonomy profiles from the moment a recruiter starts typing. <br> --- <br> # **🛡️ Redaction, Documents & Utilities — 7 tools** <br> > ### **`redact_resume`** > > Redacts personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. Configurable redaction scope. Idempotent. <br> > ### **`reformat_resume_with_template`** > > RChilli's Resume Reformatting tool accepts any structured candidate profile and applies one of six branded templates (TM001–TM006) to produce a consistently formatted output document in PDF, DOCX, RTF, or HTML — ensuring every candidate is presented in a standardized, professional layout regardless of how their original resume was structured. Designed for staffing firms, recruitment agencies, and enterprise HR teams who need to control candidate presentation at scale, it eliminates manual reformatting effort and enforces brand consistency across all submissions. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Preserves formatting fidelity. Useful as a pre-processing step before data extraction on non-standard file types. <br> > ### **`tag_entities`** > > RChilli's Named Entity Recognition tool takes already-extracted HR text and annotates it by wrapping each recognized entity in a structured XML-style label inline — returning output such as `<job_title>Senior Data Engineer</job_title>`, `<skill>Python</skill>`, `<city>Austin</city>`, `<degree>Bachelor of Science</degree>`, and `<organization>Google</organization>` — covering 10+ HR-specific entity types including person name, state, country, date, and year. Unlike data extraction tools that produce separate field lists, tag_entities preserves the full original text structure with entities labeled in place, making the output immediately consumable by ATS field-mapping pipelines, candidate profile builders, and content annotation workflows without any offset calculation or post-processing. <br> > ### **`extract_contacts`** > > Identifies and structures names, emails, phone numbers, LinkedIn URLs, and addresses with field-level confidence scores from candidate records, emails, or documents. Safe for GDPR/CCPA workflows. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Enables radius-based candidate and job search and supports workforce planning analytics. <br> > ### **`classify_job_zone`** > > RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its O/*NET Job Zone — one of five standardized levels ranging from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium preparation), Zone 4 (considerable preparation), to Zone 5 (extensive preparation required) — based on the education, experience, and training criteria defined by O/*NET. The returned Job Zone level enables downstream workflows such as candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization without any manual O/*NET lookup. <br> --- <br> # **🎯 Search & Matching — 3 tools** <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one Job Description (no index required) and returns an overall match score, dimension scores, skill gap list, and natural-language explanation. Bias-controlled and audit-ready. <br> > ### **`find_matches_in_index`** > > Accepts a resume or Job Description as input and returns the top-N most similar documents from the indexed corpus ranked by semantic similarity. No index setup required for the input document. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant's pre-populated index. Supports Boolean and semantic search modes. Requires documents to be indexed before use.
Structured web data from 31 platforms: Google, YouTube, Amazon, Walmart, Reddit, TikTok, LinkedIn
Manage 24/7 live streams, media, playback queues, schedules, and multistreaming from AI assistants.
Live Google, Bing, Amazon, Maps, Yelp & app store data. As low as $0.15/1K successes; failures free.
Stock market data for AI agents: real-time quotes, financials, options, SEC filings and news.
Amazon Seller Central + Ads: settlement-accurate P&L, full PPC, every write staged for approval.
Run Meta, Google, Amazon, and Shopify ads from Claude, ChatGPT, and Cursor. ~150 tools: read performance, inspect campaigns and audiences, research competitor ads, see ad spend vs Shopify revenue, and take confirm-gated writes (pause, budgets, campaign/ad launches) that always stage paused. OAuth 2.0 with dynamic client registration.
Real-time search API for AI agents. Search Google, Amazon, Walmart, and YouTube with 9 tools -- product search, product details, video search, transcripts, and more. Build price comparison agents, retail arbitrage tools, content research pipelines, and brand monitors. 500 free credits/month.
