"Merchant Category Code (MCC) data lookup" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
360 pay-per-call APIs for AI agents over x402 and MPP, settled in USDC on Base ($0.001-$0.25 a call, no account or API key). DeFi yields and market data, 10-chain wallet reads, wallet and token risk signals, sanctions screening, SEC financials, email/domain and web intelligence, AI text extraction, and signed proofs. Every answer names its sources and carries an evidence hash; a paid call that is not delivered is credited back. Built and run by UnyKorn LLC.
App Store Optimization data for AI agents: keyword ranks, scores, competitors, charts
Detect prompt injection, jailbreaks, and code injection in untrusted text before it reaches an LLM.
Extract structured data from invoices, receipts, PDFs, scans, photos and emails. Create an inbox for a document type, define the fields and tables to extract (or propose a schema from a sample document), upload a document and get typed JSON back, review past documents, and add an optional Python post-processing step that is tested on a real document before it is enabled. 17 tools. Sign in with OAuth by pasting your Airparser API key (Account page of the Airparser app); the client only sees that account's inboxes and documents.
EasyGerber brings AI printed circuit board (PCB) design, layout, and routing to MCP-compatible assistants. Describe a circuit or start from firmware pin assignments, search published electronic components, and build schematics using catalog parts with their footprints and available 3D models. Create and revise boards, generate layouts with automatic component placement and routing, inspect previews, and check saved layouts for blocking design issues. Export manufacturing ZIPs containing Gerber and drill files, a bill of materials, and component placement data, or download an editable EasyGerber (.egb) file. Start without signing up. Your agent creates a private guest session for one board, including catalog access, edits, layout, checks, previews, and exports. Sign in through OAuth to work with saved account boards, access permitted organization boards, or save the guest design to your account. Guest support depends on your MCP client and installed integration.
Private memory for AI agents: save and recall facts across Claude Code, Codex, Cursor and ChatGPT.
566 agent tools: chain data, prices, sanctions, web, PDF. 25 free calls, then from $0.002.
Free, read-only directory of 114 AI trading tools: broker MCP servers, market data APIs, bots and backtesters. Sourced facts on who can place orders and what safeguards exist; search, compare and check order gating for every broker MCP.
25 tools: LLM chat, summaries, translation, embeddings, speech, images. 25 free calls.
# **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.
Translation, data quality, execution verification and agent utilities over MCP + x402 USDC on Base.
Ask how ten AI engines (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, AI Mode, Copilot, Grok, DeepSeek, Meta AI) describe a brand, which sources they cite and how that changed, then build white-label client reports from the same data. 74 tools; every write is shown as a preview first and needs a second, confirmed call.
A marketplace for AI agents. Tools and data for agents, on one MCP address. No account, no API key.
13 pay-per-call tools for AI agents: LLM chat, summaries, sentiment, classification, keywords, rewriting, outlines, translation, embeddings, JSON extraction, image generation, transcription and image description. USDC via x402.
ChartGaze is a service that connects AI assistants to live and historical market data and, with your authorization, to trading-account information and trading actions. It works with MCP-compatible AI tools and supports platforms such as MT5, MT4, cTrader, and crypto exchanges.
See who spent every AI dollar. Veqen brings together work and coding agent costs per person and team, product inference costs per customer, protection incidents, invoice checks, chargeback and the month-end close. Ask your assistant to investigate recorded spending, compare model prices, review provider keys and incidents, record bills and credits, or manage supported budgets. Your account role and plan govern access. Key shut-off is handled in the Veqen app, not by this MCP connector. SDK budget blocking is not a guaranteed spending cap. Work and coding agents are free for up to 3 people; product inference is free up to $1,000 in monthly AI spend. Connect your Veqen account for private organization data. Public model-price comparisons need no account. Estimates are separate from provider invoices.
32 free MCP tools for data validation, text optimization, and synthetic test data.
Connect your health, fitness, nutrition, sleep, and wearable data to your AI assistant.
AI visibility for your brand: which buyer questions name you in ChatGPT, Perplexity, Claude and Google AI Overviews, which competitors AI recommends instead, which sources it cites, and what to fix first. OAuth sign-in with email and a one-time code.
Search and preview datasets publicly. Account-required full data via MCP.
