"How to create a game using Unity" matching MCP connectors:

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 Converts resumes, CVs, and candidate documents into structured, searchable profiles — contact details, skills, experience, education, certifications, and taxonomy-enriched data ready for ATS, HCM, and AI recruiting workflows. Used on a careers page or application form, the same 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 clear legacy databases or migration backlogs overnight. It 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, with no new document required from the candidate. This is 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 extract_resume_data. Built for pipeline automation where resumes live in cloud storage, S3, or email attachments — with the same auto-fill, multilingual, and batch-processing capabilities applied to URL-based intake. extract_job_data Converts job descriptions into structured hiring data — job title, required and preferred skills, responsibilities, experience, education, and taxonomy-normalized role requirements — ready for recruitment automation and candidate matching. 🧠 Skills & Job Taxonomy — 4 tools lookup_skill Returns the authoritative record for a known skill: description, all aliases, related skills, proficiency levels, and O*NET/ESCO mappings. Use it when you need the complete record rather than a ranked search. lookup_job_profile Returns the authoritative record for a known job profile: canonical title, SOC/O*NET code, job family, typical required and preferred skills, salary bands, and work context. autocomplete_skill Takes a partial skill string (minimum 2 characters) and returns up to 10 ranked suggestions with canonical names and categories — preventing free-text entry errors and keeping skill data clean at the point of entry. autocomplete_job_profile Takes a partial job title and returns ranked suggestions with canonical titles and job families, ensuring 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. Redaction scope is configurable, and the operation is idempotent. reformat_resume_with_template Takes any structured candidate profile and applies one of six branded templates (TM001–TM006) to produce a consistently formatted document in PDF, DOCX, RTF, or HTML — so every candidate is presented in a standardized, professional layout regardless of how their original resume was structured. Built for staffing firms, agencies, and enterprise HR teams that need to control candidate presentation at scale, it removes manual reformatting effort and enforces brand consistency across every submission. convert_document_format Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text while preserving formatting fidelity. Useful as a pre-processing step before extraction on non-standard file types. tag_entities Takes already-extracted HR text and annotates it in place, wrapping each recognized entity in a structured XML-style label — 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> — across 10+ HR-specific entity types including person name, state, country, date, and year. Unlike extraction tools that produce separate field lists, tag_entities preserves the full original text structure with entities labeled inline, making the output immediately consumable by ATS field-mapping pipelines, candidate profile builders, and content annotation workflows — with no offset calculation or post-processing. extract_contacts Identifies and structures names, emails, phone numbers, LinkedIn URLs, and addresses from candidate records, emails, or documents, with field-level confidence scores. Safe for GDPR/CCPA workflows. geolocate Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude — enabling radius-based candidate and job search plus workforce planning analytics. classify_job_zone Reads the job profile from a resume or job description and returns its ONET Job Zone — one of five standardized levels, from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium), Zone 4 (considerable), to Zone 5 (extensive preparation required) — based on ONET's education, experience, and training criteria. The returned level powers candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization, with no 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, a skill gap list, and a natural-language explanation. Bias-controlled and audit-ready. find_matches_in_index Accepts a resume or job description and returns the top-N most similar documents from the indexed corpus, ranked by semantic similarity. No index setup is required for the input document itself. search_indexed_documents Accepts a query string and returns ranked document references from the tenant's pre-populated index. Supports both Boolean and semantic search modes; documents must be indexed before use.

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

Self-hostable team wiki; agents read & write it via MCP; Atlas turns your repo into a cited wiki.

Multi-carrier shipping for AI agents: compare rates, buy labels, track packages, validate addresses

Real-time web search with answer-ready results for Claude, Cursor and any MCP client. A Tavily alternative: same speed, 20.2% fewer tokens, higher answer quality (60.7% of decided duels won) on a public benchmark. Hosted on mcp.serpdive.com or npx serpdive-mcp.

Share prototypes for team review: publish HTML, reviewers pin notes, pull feedback back to apply.

Verified local-business leads: search any niche + city, query your library, export to your CRM.

DeepMark helps teachers deliver rapid, consistent marking with meaningful feedback for every student — in a fraction of the time. What once took a week, now takes one free period.

Control Plane (controlplane.com): deploy and operate workloads across AWS, GCP, Azure, and more.

127 REST operations. 119 MCP routes; 118 JSON/text ops. OAuth 2.1. Not affiliated with X Corp.

Reassign: a circular 24-hour calendar and time-tracking copilot with ADHD-friendly scheduling.

Project portfolio management for PMOs. Alternative to Microsoft Project Online. OAuth + PAT.

Publish websites from Claude, the terminal, or CI — drop a folder, get a link that doesn't expire.

Read-only access to your bank, investment, and crypto accounts: balances, transactions, holdings.

Not another dashboard. A wealth analyst for every asset a bank can't sync, inside Claude.

Test-inbox API for email and SMS: create inboxes, long-poll messages, extract OTPs and links.

Query and join across SaaS tools, SQL, and NoSQL databases through one unified SQL interface.

Mezmo MCP is a remote Model Context Protocol (MCP) server that lets AI assistants and IDE chat agents interact with the Mezmo observability platform via the Model Context Protocol. Use it for streamlined observability, log analysis, and root-cause analysis in your favorite tools. Add Mezmo MCP and you can: 🕵️ Run advanced Root-cause analysis over recent logs 📦 List and describe Pipelines 📤 Export and filter Logs with powerful query syntax

Manage CI/CD pipelines, debug failed builds, and optimize test suites directly from your AI tools and agents - no terminal required. The CircleCI MCP Server is a remote server hosted by CircleCI that connects AI tools and agents directly to your CI/CD pipelines, giving you a conversational interface to the same pipeline, workflow, job, and artifact data you'd normally reach through the CircleCI CLI or dashboard.

Analytical memory for AI agents: a real Postgres queried in plain English over MCP. One command.