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  • Check the factual claims in a draft or an answer: a quick verdict, or a deep check with sources.

    https://lenz.io/mcp
  • Oathlayer is an independent check on AI answers. It checks each answer against independent sources, gives it a clear verification tag, and records it with a permanent doc number that anyone can look up free at oathlayer.com/verify. It reports what happened; it does not judge right or wrong. Works on any topic and with any AI model. Tool: verify_output. Connect at https://mcp.oathlayer.com. The Five Pillars of Trust: Verification Successful - Oath Approved; Verification Threshold Not Met; Various Perspectives Apply; Complex Question - Threshold Reserved; Clarity Needed to Continue. Citation Checks: paste a case citation and Oathlayer checks it against official court sources. Each citation gets a short tag: Confirmed, Unofficial, Mismatched or Not found. If trusted sources report a case was overruled, it says Reported overruled. It checks the citation only, not whether the case supports your argument. Also available as Oathlayer Chat (no programming needed) and an API (oathlayer.com/docs). Free trial: 5 free verifications, no card needed (oathlayer.com/signup). Then pay as you go: one credit per check, credits valid 12 months, no subscriptions.

    https://mcp.oathlayer.com
  • One question, one checked answer: quote-verified answers from the web and ~45 structured sources.

  • 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.

    https://www.pingaura.ai/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.

    https://mcp.rchilli.ai/mcp
  • Search Fragments — two tools for the queries an agent can't place, both built to decline rather than guess. resolve_fragment takes a half-remembered, cross-source query ("a musician who became famous for stopping performing") and returns a grounded answer, ranked web sources to confirm by eye, or an explicit no-resolution. verify_claim takes a specific factual assertion and returns supported, partially_supported, insufficient_evidence, or unsupported, with cited evidence and a stated_limits field that is always present. There is no confidence score — insufficient_evidence fires freely, and unsupported requires a source that explicitly contradicts, never mere absence of confirmation. Every verdict is decide-by-eye: "supported" means current web sources confirm it, not that the claim is true. Calibrated against 18 known claims before release. Free, no signup. Streamable HTTP (MCP 2025-11-25). Read-only.

  • Viral precedents with sourced, dated spread timelines and a Spread Score. Each answer cites a case.

    https://howitspread.com/api/mcp
  • 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.

    https://petnecto.com/mcp
  • Ask one question and get the share of people on X and Reddit who hold each answer, tied to posts.

    https://belief.openkrill.app/mcp
  • Bing search results with the ad block and Copilot answer, geo and market targeting, as JSON.

  • Growth marketing, SEO and GEO as agent tools: ranked moves, ship them, AI answer visibility.

    https://afterlaunch.io/api/mcp
  • Send your sitemap URLs to Bing, Yandex and every IndexNow engine and see each engine's own answer.

  • Lets your assistant search and read your ThinkerNotes: the insights you've written in your own words, your drafts, and your notes on books and podcasts. Ask "what have I already figured out about pricing?" and get back your own conclusions instead of a generic answer. Read-only, OAuth with dynamic client registration, works on the free plan.

    https://thinkernotes.com/mcp
  • Audits how AI answer engines read a page and grades it A+ to F across four measured layers.

    https://lekta.dev/mcp
  • Send a prompt to ChatGPT anonymously and get the answer with its web sources, as JSON.

  • What ChatGPT, Gemini and Google answer — structured JSON with cited sources.

    https://mcp.answerline.dev/mcp
  • Christian discovery for AI assistants: read any Bible passage word for word from a stored public domain King James text (never generated), find verses for what someone is carrying, get honest answers about Jesus and faith from Scripture, and find real churches, prayer gatherings and Tables near any city in the world. Every Scripture answer carries an evidence label: translation, corpus version and a content hash. Read only and free for everyone. Everything points to Jesus Christ.

    https://mcp.living-bread.org/mcp
  • Web research across search, Reddit, YouTube, local businesses, reviews, ad libraries and more. Every answer carries line-numbered citations; up to 1,000 rows per call without loading them into context.

  • Which apps connect to ChatGPT, Claude, Gemini and Grok, with steps and official sources. Read-only directory: plan required and official source for every answer.

  • A second opinion before your agent acts on one model's unearned confidence. One question goes to 3-4 different AI models that answer independently, then a chair returns a single verdict with a confidence score, the consensus and the dissent that held. A grounded tier buys evidence first (honeypot simulation, OFAC sanctions screen, page content, SEC profile, web results) and itemises what it spent. Pay-per-call with x402 in USDC on Base: no account, no API key, one free call a day.