"google trends" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Hosted Google Ads MCP: build Search campaigns and make only the changes you approve, with undo.
Google Ads MCP server for Claude, ChatGPT, Codex, Cursor and any MCP client: reports, search terms, negatives, budgets, bids, campaign creation and Performance Max, with approval on every write. Hosted by Markifact, OAuth login, nothing to install locally.
Google Ads MCP by AdPlug — a hosted server connecting Google Ads to Claude, ChatGPT, Cursor and other MCP-compatible AI assistants. Analyse live account and campaign performance, search terms, conversions and keyword data across your whole MCC. Generate reports, research keywords, run raw GAQL, and add negatives, adjust budgets or pause campaigns using natural language. Built for PPC specialists and agencies, with secure OAuth, read-only access by default and previews before changes. No coding o
Audit websites for AI search readiness and check whether a business shows up in AI answers (ChatGPT, Perplexity, Google AI Overviews). API key auth, free account at qleerly.ai.
Connect your AI assistant to your Peec AI account to monitor and analyze your brand's visibility across AI search engines like ChatGPT, Perplexity, and Gemini. Ask questions about brand visibility, competitor comparisons, source citations, and trends: all in plain language, directly from your AI tools.
# **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.
Seller-authorized, read-only business insights for Kenyan WhatsApp shops. Connect Timi to read aggregate KPIs, trends, funnel metrics, and shop health.
Search hotel prices, get best overall and best direct price in structured response. Get your developer token at https://Infoseek.ai/mcp
Lexicon Oracle is a deep-knowledge engine that analyzes population data and professional behaviors unavailable in standard LLM training. Beyond raw demographics, it specializes in predictive modeling for newer generations (Gen Z/Alpha), identifying emerging cultural trends, and forecasting the success probability of new business ventures based on behavioral market fit. Key Capabilities: Predictive Success: Forecasts business viability and market adoption. Generational Intelligence: Deep-dive ana
Scrape and extract application reviews from both the Google Play Store and the Apple App Store.
- KraceyOAuth unavailablecom.kracey.mcp
Free, read-only MCP connector for intervals.icu and HYROX training data. Connect Claude, Claude Code or ChatGPT to your own Kracey training data. Read sessions and laps, training trends, best running efforts, race predictions and saved HYROX results, or use running and HYROX calculators and find races. All tools are read-only; the connector does not build or change training plans. A Kracey account is required, but no subscription is needed for the connector.
17 data tools in one MCP server, run on Apify: Google Shopping, Flights, Hotels, News, Images, Videos, Ads Transparency, Jobs and Trends; YouTube transcripts; AI visibility checks; Google Maps leads; App Store and Google Play reviews. Pay per result. Sign in with Apify (OAuth) or an API token.
Google's video results across YouTube, TikTok, Vimeo and more: title, URL, platform, channel, duration, upload date and snippet, with duration and time filters. Runs on Apify, $1 per 1,000 videos.
Google Shopping results for any product search and country: price, original price and discount, store, delivery, returns, rating and review count, about 50 products per search. Runs on Apify, $1 per 1,000 products.
Google News for any topic or company: headline, source, publish time, snippet and the publisher's article URL, with time filters (past hour to past year) and sort by date. Runs on Apify, $1 per 1,000 articles.
Google Images for any search: full-size image URL, width, height, file size, thumbnail, title and source page, with size, color, type, time and Creative Commons filters. Runs on Apify, $0.25 per 1,000 images.
Google Hotels for any city or area: nightly prices for your dates (with taxes), star class, rating, reviews, coordinates, nearby places and photos, with rating, class and price filters. Runs on Apify, $1 per 1,000 hotels.
Google Flights for any route and date: price, airlines, flight numbers, times, stops, layovers, aircraft, legroom and CO2. One way or round trip, any cabin and currency. Runs on Apify, $0.20 per 1,000 flights.
Every ad a brand runs on Google Search, YouTube and Display, from Google's Ads Transparency Center: format, first and last shown, days running, ad text, images and links. Find advertisers by name or domain. Runs on Apify.
