"Creating an Automation System Using n8n" matching MCP connectors:

Specialist tools for any job — a lead, an image, a song, live data, and more.

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. Use Cases Resume screening — Parse and structure resumes for AI-powered shortlisting Job description analysis — Extract required skills, experience range, and qualifications from any JD Candidate-to-job matching — One-to-one fit scoring with field-level evidence, no indexing required Talent pool search — Keyword search across your indexed resume database Skill gap analysis — Identify what a candidate is missing for a specific role Bias-free hiring — Redact names, photos, gender, and age before sharing with hiring managers Taxonomy enrichment — Look up and autocomplete 10,000+ standardized skills and job titles Document standardization — Convert and reformat candidate documents into consistent templates Tools — 17 Total userkey and subuserid are injected automatically from your Bearer token — you never need to pass them manually. 🔍 Resume & Job Description Parsing — 3 tools 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). 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. 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. 🧠 Skills & Job Taxonomy — 4 tools 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. 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. 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. 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. 🛡️ Redaction, Documents & Utilities — 7 tools 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. 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. 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. 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. 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. 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. classify_job_zone: RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its ONET 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 ONET. 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. 🎯 Search & Matching — 3 tools 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. 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. 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.

Build personal interactive apps with real URLs and persistent storage, using any AI.

aX is an agent-native collaboration network. A single Streamable HTTP MCP endpoint gives agents persistent identity, real-time messaging with @mentions and threads, tasks and handoffs, shared workspace context, semantic search, agent discovery, and rendered MCP App / widget artifacts that humans and agents can open and play back.

Provide AI agents and automation tools with contextual access to blockchain data including balance…

An API for forecasting and multi-agent research. FutureSearch provides endpoints that use web research agents at scale, for higher accuracy than web search or single agent approaches alone can achieve. forecast runs a team of forecasters to predict future dates, numbers, and probabilities. multi_agent orchestrates multiple researchers to answer one question. agent_map runs one research agent over every row of a dataset, scaling to thousands of rows and agents.

Agent Interviews is an AI-powered research platform designed to conduct qualitative interviews an...

Facebook / Meta Ads automation with AI: analyze performance, test creatives, optimize spend.

Provides cloud browser automation capabilities using Stagehand and Browserbase, enabling LLMs to i…

Streamline your Attio workflows using natural language to search, create, update, and organize com…

An MCP server that provides access to Agility CMS. See https://mcp.agilitycms.com for more details.

SubDownload exposes YouTube as an MCP-native data source. Connect via OAuth and your AI agent can summarize videos, fetch full transcripts (even for videos with no captions, via AI ASR), search across channels, and save everything into a private knowledge base. Works with Claude, ChatGPT, Cursor, and 40+ MCP clients. Free credits on signup, no card required.

FFmpeg Micro MCP Server. Transcode videos from n8n or Make using FFmpeg in the cloud. Code+Docs: https://github.com/javidjamae/ffmpeg-micro-mcp/

An MCP server for deep research or task groups

Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.

Book discovery using an AI-curated book catalog that eliminates hallucinations and surfaces lesser-known titles.

Provide real-time and forecast weather information for locations in the United States using natura…

Official Miro MCP server - Supports context to code and creating diagrams, docs, and data tables.

Enable AI assistants to perform web searches using Perplexity's Sonar Pro.

Pace is a remote MCP server that exposes wearable and fitness data to Claude via the Model Context Protocol. It connects to Garmin, Oura, Whoop, Polar, Fitbit and 20+ devices and provides 15 tools for querying sleep, activity, recovery, and training data. Hosted on Google Cloud Run, OAuth 2.1 authentication, Streamable HTTP transport. Instructions: First you need to create an account at: https://pacetraining.co and connect your wearables. After that you can connect the remote Server via Custom Connector in Claude and OAuth 2.1 Flow startet.