"Maps" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
One key and one prepaid balance for the live data AI agents need: Google search results, keyword and SEO data, Google Maps places, e-commerce product prices and exchange rates. Agents find tools by job, see the price before running, and pay per call. Failed calls are free. Sign in with OAuth, no API key to copy. Early access: new accounts are approved in batches.
Solve production, blending, transport and scheduling problems with LP and MIP.
Set prices, protection levels and overbooking limits from demand and remaining capacity.
Monitor metric streams with control charts and alert only on statistically real drift.
Match bank receipts and payments to invoices with confidence, deductions and exceptions.
Generate synthetic random user data for testing, demos, and development without using real persona.
Choose and rank suppliers, size orders and classify spend from delivery and purchase data.
Analyze customer data to make segmentation and predict which customer to focus on for more sales.
Plan vehicle routes, job assignments and load packing from stops, fleet and constraints.
Prioritise overdue accounts by recovery uplift and recommend a compliant contact strategy.
EU cookie-consent & tracker audit, run like a real first-time visitor (EU proxies). Free quick check, no account. Finds trackers and tracking cookies before consent, missing first-layer Reject, what still fires after Reject, Google Consent Mode v2 state per signal, plus Google Fonts/YouTube/Maps/reCAPTCHA. Every finding comes with the exact request as evidence and a concrete fix, in 13 languages. Weekly monitoring, white-label PDF for agencies, private share links. Works with any CMP (Cookiebot, Usercentrics, Complianz, CookieYes, Borlabs…).
Live ticket market data for your AI: prices, listings, sales and seat maps for concerts and sports.
ChatGPT visibility, SERP, Maps, reviews, traffic, backlinks, tech stack, PDF tables, Amazon, jobs, Trends.
Schedule spaced reviews and exam study plans from each learner's forgetting curve.
Map prerequisites, track mastery and recommend the next topic each learner is ready for.
Analyze and score leads to identify high-potential prospects and prioritize sales outreach.
Analyze inventory levels and optimize stock allocation to reduce waste and avoid stockouts.
Analyze Product performance on Platforms like youtube, instagram etc. To optimize Marketing.
Put a map on a site with your AI: map your places, design the look, and get the code.
Forecast product demand using historical sales and market signals.
Run sandboxed Python for calculations and PDFs; no other languages, installs or network.
Indian Railways IRCTC helps you to view, track trains schedules, ticket status and more.
Detect hiring adverse impact and estimate whether a selection method is worth its cost.
# **RChilli** RChilli MCP Hub is a production-grade Model Context Protocol (MCP) server that exposes RChilli’s HR data intelligence platform through AI-callable tools. Built on 15+ years of HR data intelligence, RChilli supports ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. The tools return consistent, structured JSON responses for recruiting, talent intelligence, skills, matching, compliance, and HR workflow use cases. `userkey` and `subuserid` are injected automatically from the Bearer token and must not be passed manually. <br> --- <br> # **Available Tools** <br> > ### **`analyze_hr_call`** > > Transcribes and analyzes HR or recruiting call recordings to extract speaker turns, sentiment, candidate intent, action items, skill mentions, and structured follow-up data for recruiting operations. <br> > ### **`analyze_skill_adjacency`** > > Identifies related, adjacent, and transferable skills to support skill discovery, talent mobility, workforce planning, and candidate evaluation. <br> > ### **`analyze_skill_gap`** > > Compares candidate or employee skills against a target job and returns present skills, missing skills, readiness score, and development areas for hiring, internal mobility, and workforce planning. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to standardized taxonomy profiles from the moment a recruiter starts typing. <br> > ### **`autocomplete_skill`** > > Accepts a partial skill string and returns ranked autocomplete suggestions with canonical skill names and categories. Helps prevent free-text entry errors and keeps skill data clean. <br> > ### **`classify_job_zone`** > > Reads the job profile from a resume or job description and returns its O*NET Job Zone, from Zone 1 through Zone 5, based on the education, experience, and training preparation required for the role. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Useful as a pre-processing step before extracting data from non-standard document formats. <br> > ### **`detect_bias`** > > Reviews job descriptions and hiring criteria for gendered language, age-coded terms, unnecessary credential inflation, cultural exclusion signals, and related compliance risks. Suggests neutral replacements. <br> > ### **`detect_resume_suspicion`** > > Reviews candidate profile data for possible inconsistencies, unusual claims, timeline conflicts, missing context, and other signals that require recruiter review. <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. <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. <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. Supports multilingual extraction, batch processing, resume auto-fill, and reprocessing of historical resumes. <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 Resume Data Extraction. Ideal for resumes stored in cloud storage, S3, email attachments, or other URL-accessible sources. <br> > ### **`find_matches_in_index`** > > Accepts a resume or job description as input and returns the top matching documents from the indexed corpus, ranked by semantic similarity. <br> > ### **`generate_interview_questions`** > > Creates technical, behavioral, situational, and competency-based interview questions tailored to a role and candidate profile, including question type, competency, and expected answer signals. <br> > ### **`generate_jd`** > > Generates structured job descriptions with title, responsibilities, required skills, preferred skills, qualifications, tone alignment, language support, and inclusivity scoring from role inputs. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Supports radius-based candidate search and workforce planning. <br> > ### **`index_document`** > > Indexes a resume or job description in RChilli Search & Match so it can be searched, matched, updated, and reused across candidate matching, job matching, and similarity-search workflows. <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> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, aliases, related skills, proficiency levels, and O*NET or ESCO mappings. <br> > ### **`map_job_to_skills`** > > Converts job descriptions into granular skill maps with required and preferred skill clusters, proficiency signals, taxonomy tags, and downstream matching attributes. <br> > ### **`match_end_to_end`** > > Orchestrates data extraction, indexing, taxonomy normalization, and matching to return ranked candidates or jobs with scores, explainable results, and an audit trail from a single call. <br> > ### **`normalize_candidate_data`** > > Cleans and normalizes candidate records from multiple ATS or HR systems, resolves field conflicts, deduplicates records, and produces standardized candidate data with a field-level change log. <br> > ### **`normalize_hr_data_with_lov`** > > Maps raw and inconsistent HR data values, including degrees, universities, certifications, job profiles, skills, and locations, to the closest configured List of Values entry with a standardized value and confidence score. <br> > ### **`recommend_learning_path`** > > Builds structured learning paths with prerequisites, skill sequence, certifications, proficiency targets, and LMS alignment based on a person’s profile and target role. <br> > ### **`redact_resume`** > > Redacts configured personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. <br> > ### **`reformat_resume_with_template`** > > Applies one of the RChilli resume templates, TM001 through TM006, to a structured candidate profile and produces a consistently formatted PDF, DOCX, RTF, or HTML document. <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one job description and returns an overall match score, dimension scores, matched skills, skill gaps, and a natural-language explanation. No index is required. <br> > ### **`screen_application_batch`** > > Evaluates a batch of candidates against job requirements and custom criteria, returning scores, insights, skill gaps, and recommended next actions for each candidate. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant’s indexed corpus. Supports Boolean and semantic search modes. <br> > ### **`skill_mapping`** > > Maps raw skill text to standardized RChilli taxonomy skills, aliases, classifications, and related skill information. <br> > ### **`tag_entities`** > > Annotates extracted HR text by wrapping recognized entities in structured XML-style labels. Supported entities include job titles, skills, names, degrees, organizations, locations, countries, dates, and years.
Google search, news, maps, webpages, cited answers, research and ChatGPT/Gemini answers for agents.
Location tools for Claude and other MCP clients, strongest in the US and Great Britain. Geocode whole lists with a confidence score per row, filter by distance, get road distances and drive-time areas, and plan multi-driver routes with Google Maps links. Check flood zones, crime and live warnings, find nearby places and UK registers (NHS, CQC, food hygiene), and keep shareable maps. Reads and writes Google Sheets. Free: sign in with Google.
Analyse tests for item quality, true ability, reliability and learner mastery.
Exact arithmetic, algebra, calculus, physics, chemistry and finance with a checkable formula.
Generate marketing images, videos and audios for campaigns, product content, and brand assets.
Flett is a shared online whiteboard. Connect it to Claude, ChatGPT or Cursor and the AI draws straight onto your boards: flowcharts, mind maps and architecture diagrams made of shapes and arrows, documents, slides and designed pages, charts, maps and files. Everyone on the board sees it appear live and can move, edit and comment on everything, then present it or export it to PNG, PDF, PowerPoint or HTML. Sign-in is OAuth; reading boards is free.
