"Creating an application" matching MCP connectors:
GET /v1/connectors – MCP directory API referenceMatching Connector Tools:
Machine-exclusive web distillation MCP server for AI agents. Deterministically strips 85-97% of HTML/DOM/JS/CSS token bloat into clean Markdown, structured JSON tables/JSON-LD, <200-token structural skeletons, or BM25 budget-capped context with native HTTP 402 (x402 Base USDC & L402) micropayments.
Ask questions about Claude Code, Cursor, Codex and MCP — get answers grounded in their official docs, with citations. A hybrid RAG service that AI agents call through two MCP tools (or over REST). It indexes ~600 pages of official documentation, refreshes itself daily, and every design choice is backed by an experiment.
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.
Document to Markdown: convert Word, Excel, PowerPoint and PDF files to Markdown for an LLM.
Ask a UK fractional CTO's practice directly: services, prices, articles, and whether he fits.
Lean operations knowledge base built on DBS: cited answers, tool cards, plans and diagnoses.
CVE, KEV, MITRE ATT&CK, CWE and detection graph for AI agents; every link names its source.
MarkupBase turns AI-generated Markdown and HTML into durable, versioned artifacts that people can review and discuss. Its MCP server lets agents publish new versions, preserve contextual comments, include hosted images, and respond to feedback through secure account-linked identities, creating a clear human review boundary without requiring real-time editing.
Enhanciar is a company brain for engineering teams. It ingests your GitHub repos, Slack, Notion, Google Docs, Jira/Linear and PDFs into a cited wiki and knowledge graph, and answers questions from any MCP client with every claim linked to the source line, message or page. Tools: query (cited Q&A), search_wiki, get_page, list_pages, get_graph, get_process_map, impact (blast radius of changing a file or function), list_repos, list_skills/get_skill, propose_action/list_proposed_actions (draft Jira/Linear/Slack/calendar actions for human approval). BYOK — bring your own model key. Early access: join the waitlist at https://enhanciar.in and create an API key in Settings.
The only News based AI MCP your agents will ever need — custom categories, global regions, and time-scoped results in one tool. We use multi-vector & sparse-hybrid search to search through thousands of articles across the world to find the exact news you're looking for.
Clean rows from public pages that break ordinary scrapers, plus a bounty board where agents earn.
Build and test an AI front desk for a service business from its website, before signing up.
株式会社アイスター商事の公式サイトです。（ai-star.co.jp の内容を検索して答える。ヨミタス経由）
The context marketplace: expert guided journeys your assistant runs step by step.
Serve a folder of Markdown notes as an MCP server: hybrid search, reading, and sourced answers.
Generate 18 AI readiness files (llms.txt, ai.txt, RAG indexes, schema) for any website.
Desk keeps a support team’s approved answers, refund rules, and escalation limits, then lets an assistant read that policy before it replies. A 14-day trial, then Pro. The price is only at checkout.
Read-only Chinese Vibe Coding knowledge connector for search, docs, and repo freshness.
x402 clean article text and metadata for AI/RAG. $0.002 per successful article.
x402 PDF text, pages, and metadata for AI/RAG. $0.0015 per successful PDF.
