"Services that provide internet connectivity" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Bring professional data and tools to the AI you already use. QVeris helps AI assistants, products, and workflows find services, review supported scope, call them, and audit usage.
Connect AI agents to the health, screen-time and recorded location data you choose to share from myself.md. Discover schemas, query connected phones, and read paginated JSON or JSONL cloud exports. OAuth sign-in and explicit data grants are required. Phone requests depend on connectivity and OS permissions; cloud reads require existing storage access. Owners can revoke sharing; revocation does not erase recipient copies. Privacy: https://myself.md/privacy · Terms: https://myself.md/terms · Support: cody@isolated.tech
Search over 1.85 million captioned images using text, a reference image, or both. Upload a JPEG, PNG, or WebP image as base64, or provide a public HTTPS image URL. Find similar images from a Lightdrift asset ID and retrieve hosted file URLs, source licenses, and attribution. Connect with OAuth or an API key. Searches cost $0.005 ($5 per 1,000); image details are free. Setup: https://docs.lightdrift.ai/guides/images-mcp
Give your AI a network of real people. Marcel is an MCP server that lets your AI find and connect you with people who can help. Connect Marcel to Claude, ChatGPT, Codex, Cursor, or another MCP-compatible AI and ask things like: “Who can help me with B2B SaaS distribution?” “Find someone who has hired a customer success team.” “Do I have any new connection requests?” Marcel uses your profile and network to find relevant people and explain why an introduction makes sense. No social feed. No f
Videos that make your business go viral, made for you.
Zyte MCP is a remote MCP server that lets an agent use Zyte on your behalf. It can download pages with a plain HTTP request or in a real browser, click and type and take screenshots, and pull structured data out of a page with AI, either as a standard type like product, article or job posting or as fields you describe in plain words. It can also search the web from a chosen country, check whether Zyte API supports a site and what a request costs, and report your usage by domain. If you have Scrapy Cloud projects, the same server can list spiders, start and cancel jobs, read logs and items, manage schedules and settings, and read and write collections. Those tools can change and delete things, so review what your agent asks to do. Connect to https://mcp.zyte.com/v1/mcp and sign in to your Zyte account with OAuth. Using the server is free. What your agent does through it is billed at Zyte API prices, on a dedicated mcp_access_key you can track and cap. New accounts get $5 of trial credit for a month.
IconScout MCP lets AI coding tools like Claude, Cursor, VS Code, and Devin pull icons, illustrations, 3D assets, and Lottie animations directly while building. The difference from plain asset search is that it picks assets that match the style of the site or UI you're working on, so everything stays visually consistent.
Plot turns the trips you plan with an AI assistant into a real, shared itinerary. Connect it and your assistant can save flights (every leg), hotels, restaurants, activities, trains and car rentals to the right day in local time, read your trips back, and update or delete bookings. Sign in with your Plot account; there are no API keys. Works with ChatGPT, Claude, Muse and any client that supports remote MCP.
fomox402 is a last-bidder-wins on-chain game on Solana, designed to be played autonomously by AI agents. Every bid mints a key that earns passive $fomox402 dividends; the last bidder when the timer hits zero wins the pot. Drop our MCP server into Claude Desktop, Cursor, Goose, or any HTTP-speaking client to play.
aX is an agent-native collaboration network. A single Streamable HTTP MCP endpoint gives agents persistent identity, real-time messaging with @mentions and threads, tasks and handoffs, shared workspace context, semantic search, agent discovery, and rendered MCP App / widget artifacts that humans and agents can open and play back.
mrmarket.ai is a remote MCP server that resolves financial research questions against clean, structured market data. Multi-factor screens, rankings, cohort-relative comparisons, point-in-time event studies, and forward-return analysis across 11,000+ US-listed tickers, returned as structured rows.
Stop paying for your agent to rediscover what other agents already figured out. Prior is a shared knowledge base where agents exchange proven solutions — one search can save 10 minutes of trial-and-error and thousands of tokens. Your Sonnet gets access to solutions that Opus spent 20 tool calls discovering. Search is free with feedback, and contributing earns credits.
Visual AI prompt builder that decomposes any raw prompt into 12 semantic blocks (role, context, objective, constraints, examples, etc.) and recompiles them into Claude-optimized XML. Exposes decompose_prompt and compile_prompt tools.
Tribeunal turns a question into a jury's verdict. An agent opens a case, a jury of humans and AI agents is seated, evidence is weighed and votes are cast, and the tally becomes a ruling the agent can long-poll for and act on. 39 tools, all annotated, plus eight Agent Skills that carry the procedure: how big a jury a decision needs, when a verdict actually lands, how to act on it. Arbitration mode bars the case owner from voting or closing early and enforces a quorum, closing with a verdict.
# **RChilli MCP Hub** RChilli MCP Hub is a production-grade MCP server that exposes RChilli's full HR data intelligence platform as 17 AI-callable tools across 4 categories. Built on 15+ years of HR data intelligence, it is trusted by ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. Every tool is read-only and returns a consistent, structured JSON response — no raw exceptions, no inconsistent formats. <br> --- <br> # **Tools — 17 Total** userkey and subuserid are injected automatically from your Bearer token — you never need to pass them manually. <br> --- <br> # **🔍 Resume & Job Description Parsing — 3 tools** <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. When used on a careers page or application form, the same extraction call auto-fills every application field in under 10 seconds — documented to increase candidate conversion by up to 194%. Supports 40+ languages with English-normalized output for global intake, and runs in batch mode to process legacy databases or migration backlogs overnight at scale. Also supports resume reprocessing — re-running previously extracted resumes through the latest extraction logic and taxonomy version to bring older records up to current data quality, without requiring a new document from the candidate. Distinct from bulk import (first-time extraction of a new batch) and from talent data refresh (re-enrichment from a newer submitted resume). <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 the Resume Data Extraction tool. Ideal for pipeline automation where resumes are stored in cloud storage, S3, or email attachments. Also supports the same auto-fill, multilingual, and batch-processing capabilities as the core extraction tool for URL-based intake sources. <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 for recruitment automation and candidate matching. <br> --- <br> # **🧠 Skills & Job Taxonomy — 4 tools** <br> > ### **`lookup_skill`** > > Returns authoritative detail for a known skill including description, all aliases, related skills, proficiency levels, and O*NET/ESCO mappings. Use when you need the complete record rather than a ranked search. <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> > ### **`autocomplete_skill`** > > Accepts a partial skill string (min 2 chars) and returns up to 10 ranked autocomplete suggestions with canonical names and categories. Prevents free-text entry errors and keeps skill data clean at point of entry. <br> > ### **`autocomplete_job_profile`** > > Accepts a partial job title string and returns ranked autocomplete suggestions with canonical titles and job families. Ensures job titles map to taxonomy profiles from the moment a recruiter starts typing. <br> --- <br> # **🛡️ Redaction, Documents & Utilities — 7 tools** <br> > ### **`redact_resume`** > > Redacts personally identifiable information from candidate profiles to support anonymized review, bias-aware screening, compliance workflows, and audit logs. Configurable redaction scope. Idempotent. <br> > ### **`reformat_resume_with_template`** > > RChilli's Resume Reformatting tool accepts any structured candidate profile and applies one of six branded templates (TM001–TM006) to produce a consistently formatted output document in PDF, DOCX, RTF, or HTML — ensuring every candidate is presented in a standardized, professional layout regardless of how their original resume was structured. Designed for staffing firms, recruitment agencies, and enterprise HR teams who need to control candidate presentation at scale, it eliminates manual reformatting effort and enforces brand consistency across all submissions. <br> > ### **`convert_document_format`** > > Accepts a document as base64 or URL and converts between PDF, DOCX, RTF, HTML, and plain text. Preserves formatting fidelity. Useful as a pre-processing step before data extraction on non-standard file types. <br> > ### **`tag_entities`** > > RChilli's Named Entity Recognition tool takes already-extracted HR text and annotates it by wrapping each recognized entity in a structured XML-style label inline — returning output such as `<job_title>Senior Data Engineer</job_title>`, `<skill>Python</skill>`, `<city>Austin</city>`, `<degree>Bachelor of Science</degree>`, and `<organization>Google</organization>` — covering 10+ HR-specific entity types including person name, state, country, date, and year. Unlike data extraction tools that produce separate field lists, tag_entities preserves the full original text structure with entities labeled in place, making the output immediately consumable by ATS field-mapping pipelines, candidate profile builders, and content annotation workflows without any offset calculation or post-processing. <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. Safe for GDPR/CCPA workflows. <br> > ### **`geolocate`** > > Converts partial or informal location text into structured city, state, country, ISO codes, latitude, and longitude. Enables radius-based candidate and job search and supports workforce planning analytics. <br> > ### **`classify_job_zone`** > > RChilli's Job Zone Classification tool reads the job profile from a resume or job description and returns its O/*NET Job Zone — one of five standardized levels ranging from Zone 1 (little or no preparation required) through Zone 2 (some preparation), Zone 3 (medium preparation), Zone 4 (considerable preparation), to Zone 5 (extensive preparation required) — based on the education, experience, and training criteria defined by O/*NET. The returned Job Zone level enables downstream workflows such as candidate-to-role fit filtering, compensation benchmarking, over/under-qualification flagging, and job architecture standardization without any manual O/*NET lookup. <br> --- <br> # **🎯 Search & Matching — 3 tools** <br> > ### **`score_resume_against_jd`** > > Accepts one resume and one Job Description (no index required) and returns an overall match score, dimension scores, skill gap list, and natural-language explanation. Bias-controlled and audit-ready. <br> > ### **`find_matches_in_index`** > > Accepts a resume or Job Description as input and returns the top-N most similar documents from the indexed corpus ranked by semantic similarity. No index setup required for the input document. <br> > ### **`search_indexed_documents`** > > Accepts a query string and returns ranked document references from the tenant's pre-populated index. Supports Boolean and semantic search modes. Requires documents to be indexed before use.
Check shipping costs for all logistics services in Indonesia.
Manage CI/CD pipelines, debug failed builds, and optimize test suites directly from your AI tools and agents - no terminal required. The CircleCI MCP Server is a remote server hosted by CircleCI that connects AI tools and agents directly to your CI/CD pipelines, giving you a conversational interface to the same pipeline, workflow, job, and artifact data you'd normally reach through the CircleCI CLI or dashboard.
AI employees that run your CRM, invoicing, email, documents, dashboards and agents
Gnosari is a free AI chatbot for your website that collects data. Visitors chat, and every conversation becomes a typed, structured record: name, email, budget, timeline, whatever you asked for. The Gnosari MCP server lets Claude, ChatGPT, Cursor or any MCP client create and run your Gnosari chatbots through natural conversation. Create Gnosaris, define what data to collect, share conversation links, and review the structured data your conversations capture, without leaving your AI chat. Everyt
MCP-native AI SRE. Exposes your production OpenTelemetry problems, traces, and logs over the Model Context Protocol, plus an AI remediation loop that opens a reviewed GitHub fix PR and verifies in production (reopening on regression). Tools include list_problems, get_problem, query_traces, detect_anomalies, and request_problem_remediation. Human-in-the-loop by default — the merge button stays yours.
