"Google Forms" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Quotations, invoices and 8 more document kinds; quote to invoice; tax for 195 countries; forms.
Create, update and message Apple Wallet and Google Wallet passes and loyalty cards.
Ask your Search Console data in plain language and get real numbers, not guesses. The GSC Wizard MCP plugs your live Google Search Console data straight into Claude, ChatGPT, and any MCP-capable assistant - so the model reads your actual clicks, impressions, position and CTR instead of inventing them.
Run Google Ads, Meta Ads, LinkedIn Ads and ChatGPT Ads from Claude or ChatGPT. You approve every change.
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
Connect AI assistants to Chasync CRM for leads, funnels, forms, email templates and analytics.
# **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.
Extract structured, schema-validated data from any document, including PDFs, scans, images, spreadsheets, and forms, directly inside the chat.
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.
Google Ads, Meta Ads, GA4, Search Console and Shopify insights, alerts, ROAS rules and ad actions.
Search hotel prices, get best overall and best direct price in structured response. Get your developer token at https://Infoseek.ai/mcp
clariBI is an AI-powered business intelligence platform built for small and mid-sized SaaS, ecommerce, and operations teams. Instead of writing SQL or building dashboards manually, users ask questions in plain English ("what's our CAC by channel last quarter?") and the AI engine pulls the data from Stripe, HubSpot, Google Analytics, ad platforms, Jira, and 30+ other sources via the open Model Context Protocol. Dashboards generate themselves on source connect.
Scrape and extract application reviews from both the Google Play Store and the Apple App Store.
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 what customers say in public — Google Business profiles and reviews, Trustpilot, Tripadvisor, hotel listings and the Q&A under a listing. **What you can ask for** • "Pull this business's Google reviews and group the complaints." • "What do Trustpilot reviewers say about this competitor?" • "Find hotels matching this search and their live prices." • "What questions are people asking on this Google listing, and are they answered?" • "Search listings for this category in this city." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-business/mcp and sign in with OAuth — there is no key to create or paste. 22 tools: Google Business info, reviews, extended reviews, updates and Q&A; Trustpilot and Tripadvisor search and reviews; Google hotel info and searches; business listing search; plus Reddit and Pinterest signals for a business. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Read the reviews here, then ask the same agent what that business ranks for or who links to it — 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.
Ask TalentLyft the recruiting-ops questions dashboards miss by connecting candidates, applications, activities, jobs, stages, requisitions, status logs, members, departments, pipelines, job-board posts, forms, events, and rejection reasons. Find stale applications by stage and owner, follow-up gaps from activity history, requisition stalls, source-quality movement, job-board visibility issues, disposition drift, and bottleneck owners. No dashboard build. No SQL.
Ask Factorial ATS the recruiting-ops questions dashboards miss by connecting applications, phases, candidates, sources, feedback, evaluation forms, job postings, messages, questions, answers, and rejection reasons. Find applications aging in phase, feedback debt by role and posting, source-quality gaps, rejected-candidate hygiene, incomplete application data, hiring-stage bottlenecks, and owner queues. No dashboard build. No SQL.
Give Claude only the Google Drive files you choose. Every action logged.
