"Answer" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Check the factual claims in a draft or an answer: a quick verdict, or a deep check with sources.
Cross-retailer product catalog: one scored answer per product with live prices and buy links.
Liliphant joins your meetings and remembers the work across all of them. Most tools stop at one transcript. Liliphant connects every conversation, so you can ask what was decided, who promised what, what changed since the first meeting, or what's still blocked. Every answer comes with the exact quote, the speaker and the meeting it came from, and if something is unclear, Liliphant tells you. Open promises and unanswered questions stay in view until someone marks them done.
Open forms, surveys and approvals by link: people answer on their phone, your agent gets JSON.
Hosted MCP server for Indian NSE/BSE stock research: 59 tools for quotes, financials and ratios, peers, shareholding and pledge, filings with extracted facts, screens, F&O and FII/DII context. Source and freshness on every answer; no buy/sell calls.
See how AI answer engines mention your brand: visibility scores, competitors, prompts, site health audits and AI-optimised articles, from your PingAura workspace. OAuth sign-in; read-only or read-and-write access chosen at consent.
Free, cited reference data for US law-firm marketing: lawyer advertising rules for all 50 states and DC, bar ad-filing fees, contingency fee caps, ethics opinions (incl. AI), ad-platform policies, federal MDL dockets, federal civil filings, statutes of limitations, law-office competition and legal deserts by county, lawyer wages, fatal crashes, CFPB, EEOC and FCC complaint data. Every answer carries its source, DOI and the date it was checked. Main server: 24 tools; one more server per dataset at /mcp/<dataset>. Read-only, no sign-in, CC BY 4.0. Published by Probative Growth, a marketing agency for law firms. Not legal advice.
Causal analytics: ask a business question, get a deterministic causal answer and charts.
Ask a person on an Apple device or in a browser, then read their answer.
Economic and social data for 298 countries and regions: 754 indicators in 10 categories — macroeconomics, financial markets, labor, demographics, real estate, commodities and energy, transport, healthcare, education and technology. Sources include the World Bank, IMF, FRED, OECD and WHO. Every answer carries the source, the licence and a link to the EconDash chart; IMF and other forecasts are labelled and kept apart from observed values. Five read-only tools, no sign-in.
SpiderLook collects your rivals' social posts, ads and website changes daily and lets your AI assistant answer what changed, why it matters and what to do next.
OptimistFi gives AI assistants live perpetual futures data across 40 venues: funding on every venue, open interest, leverage and liquidation levels, whale trades, order-book depth and the net position of Hyperliquid's largest accounts, with up to 90 days of history. It also covers the rules and live challenge prices of 21 perps prop firms, and a dated, SEC-sourced investment thesis for more than 6,100 US stocks. Public and read-only: no key, no sign-up. Every answer names its source and time.
111 tools for AI assistants, so an answer comes from a rule instead of a guess. 84 deterministic utilities. IBAN and VAT validation, cron parsing, regex testing, hashing, JWT decoding, conversion between JSON, CSV, XML and YAML, units, number bases, timezones and date arithmetic. Eleven of them encode Dutch rules that a model tends to get wrong: AOW pension age, inheritance and gift tax, unemployment benefit duration, notice period, severance, the 30 percent ruling, holiday allowance, mileage allowance, and public and school holidays. Five more generate test data that is structurally valid and belongs to nobody: BSN, IBAN, BRP person records, UPA pension files and document numbers. 14 render tools that return a hosted file. HTML or a live URL to PDF, full page screenshots, QR codes, PDF merge and split, images to PDF, image resize, convert and compress, metadata stripping, PDF to DOCX and back. Plus read_page, which returns the readable content of a JavaScript rendered page as Markdown. 4 memory tools give an agent key-value state that survives between sessions, scoped to its own API key. 3 workflow tools list and run saved multi-step workflows on the caller's account. 6 AI tools for the jobs where a model is the right instrument rather than the wrong one: check a message for scam patterns, write a spreadsheet formula, turn a question into SQL, translate code between languages, improve a prompt, draft an email. Utilities and memory are free. Renders and AI tools have a free tier and credit packs. Workflows need an API key, because a workflow belongs to an account rather than to a key. Streamable HTTP at https://toolforte.com/api/mcp
# **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.
Connect Chatway, an AI-powered website live chat and customer support platform, to ChatGPT, Claude, Gemini, Cursor, and other AI assistants using MCP. Chatway helps businesses communicate with website visitors, manage customer conversations, and deliver faster support across multiple channels, including website live chat, email, WhatsApp, Instagram, and Facebook Messenger. Native mobile apps for iOS and Android let support teams manage conversations and respond to customers on the go. With Chatway's MCP server, AI agents can access and manage customer support operations. Search and read conversations, send replies, add private notes, resolve and assign chats, manage contacts, retrieve customer information, and search knowledge base articles. Chatway also includes a built-in AI Support Agent for automated customer service, a shared team inbox, support ticket management, a customizable website chat widget, a help center, team management, automated workflows, visitor tracking, and integrations with popular platforms, including WordPress and Shopify. Connect external AI agents to automate customer support, answer customer questions, access conversation history, and streamline support workflows without building custom integrations. Manage your customer support from anywhere using Chatway's web dashboard or mobile apps. The hosted MCP server supports Streamable HTTP and secure OAuth 2.1 authentication with scoped permissions. Available on all Chatway plans, including the Free plan. Conversation limits apply. Website: https://chatway.app Documentation: https://developers.chatway.app/mcp/guides.mcp_overview
Public Q&A board where AI agents post questions they are stuck on and other agents answer.
Viral precedents with sourced, dated spread timelines and a Spread Score. Each answer cites a case.
Paw to paw. Shelter pets, one question away. Ask in plain words, in any of ten languages ("Use Petnecto to find me a calm older dog near Seattle that is good with cats") and get real adoptable pets near the place you name, with photos, ages, sizes, what each rescue says about them, and exactly how to reach the rescue. Listings come from open public data that shelters and governments publish (Taiwan's national adoption listings, King County WA and Montgomery County MD in the US, Zaragoza and Vienna in Europe), credited in every answer. Adoption always happens through the rescue: Petnecto takes no payments, applications or personal details, and needs no account.
Connect your AI assistant to your Quattr account and ask in plain English: who gets cited instead of you in AI answers, why organic clicks changed, how you compare with tracked competitors, and which pages need attention next. Every answer comes from your own Quattr data.
Find buyers, run LinkedIn, email and X outreach, and answer replies from your AI.
