"Development-related MCP resources" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
NSW DAs, CDCs and property planning reports: zoning, height, FSR, nearby DAs. Pay per call via x402.
Generate synthetic random user data for testing, demos, and development without using real persona.
Read Schedule it events, resources, groups and labels; create and update events and resources.
Google Trends interest over time, by region, and related queries and topics, as JSON.
MCP server for LingoHub — localization projects, resources, exports and repository pushes.
Your job is to win new business, not to spend hours researching medical device companies. Whimbrel saves you hours of research inside the AI app you already use. Whimbrel is for sales and business development teams at firms that sell to medical device companies: contract manufacturing, sterilization, testing, design, regulatory and software services. Every fact links to its public source. Your first month is free. https://whimbrelresearch.com/
Your job is to win new business, not to spend hours researching medical device companies. Whimbrel saves you hours of research inside the AI app you already use. Whimbrel is for sales and business development teams at firms that sell to medical device companies: contract manufacturing, sterilization, testing, design, regulatory and software services. Every fact links to its public source. Your first month is free. https://whimbrelresearch.com/
Review Cliento locations, staff, resources, services, products and stock.
Read Hub Planner resources, projects, bookings and timesheets; create clients and time entries.
Search, read and save your Linko notes and resources (AI knowledge base) from any AI chat.
# **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.
Spec-driven development for AI coding agents: live requirements, stories and traceability over MCP
Access Zain Ahmed’s public engineering profile, project case studies, technical articles, and platform engineering resources through MCP tools and structured resources. Hosted Streamable HTTP endpoint at zainahmed.net; public retrieval requires no API key.
Unblocked is the context layer for agentic software development.
Query official international development indicators from sources including the World Bank, WHO, UNICEF, FAO, ILO and IMF, preserving definitions, units, source identifiers and citations
MCP server exposing T54 XRPL x402 seller SKUs (OpenAPI) as tools: structured query, research brief, commerce data, and related routes. HTTP 402 + x402 settlement via broker (XRPL / Base USDC per env). Unified HTTPS host with health at /health.
Collaborative intelligence platform where AI agents and humans share findings, discuss ideas, and build knowledge together. 7 tools for searching posts, creating content, commenting, voting, messaging, notifications, and browsing the agent/human directory. Plus resources for browsing latest posts, colonies, and trending tags.
Your agent needs to crawl a site and say what is wrong with it — broken tags, duplicate content, pages nothing can index, resources that never load. **What you can ask for** • "Crawl this site and list every page with a duplicate title or missing description." • "Which pages are non-indexable, and why?" • "Run Lighthouse on these URLs and give me the failing audits." • "Show the internal link graph and the orphan pages." • "Give me this page's raw HTML and its microdata." **How to use it** Point any MCP client at https://mcp.aisa.one/seo-onpage/mcp and sign in with OAuth — there is no key to create or paste. 20 tools: submit a crawl and read its summary, pages, resources, links and waterfall; duplicate content and duplicate tags; keyword density; non-indexable and uncrawlable resources; parsed content, raw HTML, microdata, screenshots and Lighthouse. **Why this rather than the source** A crawler you drive from the agent, with the audit results as structured data rather than a PDF. **It is also a door to the rest** The same login reaches 26 sources and 580+ operations. Find the broken pages here, then ask the same agent what those pages used to rank for — 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 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.
The official Svelte MCP server providing docs and autofixing tools for Svelte development
