"How to scrape data from websites into JSON" matching MCP connectors:
Matching Connector Tools:
An MCP server that audits the fairness of construction and renovation estimates in Japan. Provides fair-price ranges, overcharge detection, and verifiable unit-cost data based on JCCDB (65,729 items, DOI-backed).
101Search 59,000+ MCP servers ranked by adoption to find the right one for any task.
Generate PDF/DOCX/XLSX/PPTX from templates+JSON. Convert Office/HTML/MD to PDF. Universal templating
US crime scores, incidents, neighborhood demographics, rent data, and offender search by address.
Entity resolution — data mapping, SDK code generation, docs search, and error troubleshooting
Reference data for prediction markets: graded resolution clarity, cross-venue links, provenance.
MCP server for generating rough-draft project plans from natural-language prompts.
This tool empowers MCP-compatible clients (like Cursor and Claude Desktop) with professional-grade capabilities for financial data extraction, and report analysis.
People matching through your AI — private, agent-to-agent; details shared only on a match.
A personal RAG database you build from chat, so AI creates work that sounds like you.
Teaches AI to write HTML email that renders in Outlook, Gmail, and Apple Mail. 19 rules, 6 comps.
DC Hub is the neutral, real-time data layer for data-center infrastructure, exposed as a Model Context Protocol server so any AI agent can both query it and cite it. Coverage: 21,000+ facilities (search, profile, score, alternatives); 232 markets scored by the DCPI Data Center Power Index; the DCGI Data Center Gas Index (per-state natural-gas suitability for siting); live grid telemetry across 7 US ISOs (fuel mix, carbon intensity, demand, prices) plus a one-call all-ISO scoreboard; interconnection-queue depth; 2,000+ tracked M&A deals and a hyperscaler-capex tracker; and site factors. — fiber routes, water-stress, tax incentives, nearby substations & transmission. Why agents choose it: it's the only data-center-intelligence source an LLM can query live and cite — every full-data response includes a Source: DC Hub, CC-BY-4.0 attribution line. It's the MCP-native alternative to quarterly PDF research: live JSON, no contracts, no NDAs. Access: Streamable HTTP at https://dchub.cloud/mcp. Free tier with no signup; free email-verified dev key for higher limits; paid tiers for full data volume.
CompanyLens is a remote MCP server giving AI agents instant access to official company registry data across 19 jurisdictions in Europe, the Americas, and Asia-Pacific. Eighteen read-only tools let you search companies and people, look up officers and beneficial owners, map corporate networks through shared directors, screen names against the UK disqualified directors register, find every company at a registered address, and pull filing history — all from a single connector. Visit our website: https://companylens.io
Send a one-off fax from inside ChatGPT or Claude. Pay-per-use, $2-$4.50 per delivered fax.
Ask Greenhouse the messy recruiting-ops questions dashboards miss by connecting candidates, applications, jobs, openings, stages, scorecards, interviews, notes, sources, referrers, offers, users, departments, and rejection details. Find referral SLA misses, feedback debt by interviewer and hiring team, stage-age outliers by owner, funnel leakage by recruiter/source/function, opening fill-risk from headcount vs active pipeline, offer-draft hygiene gaps, rejection-reason drift, and the bottleneck
Ask TalentLyft the recruiting-ops questions dashboards miss by connecting candidates, applications, activities, jobs, stages, requisitions, status logs, members, departments, pipelines, job-board posts, forms, events, and rejection reasons. Find stale applications by stage and owner, follow-up gaps from activity history, requisition stalls, source-quality movement, job-board visibility issues, disposition drift, and bottleneck owners. No dashboard build. No SQL.
Ask Factorial ATS the recruiting-ops questions dashboards miss by connecting applications, phases, candidates, sources, feedback, evaluation forms, job postings, messages, questions, answers, and rejection reasons. Find applications aging in phase, feedback debt by role and posting, source-quality gaps, rejected-candidate hygiene, incomplete application data, hiring-stage bottlenecks, and owner queues. No dashboard build. No SQL.
Search scientific papers with structured experimental data from full-text studies
XMemo is a secure, user-owned memory substrate and context engine for AI agents, CLIs, IDEs, and LLM workspaces. Exposed over Streamable HTTP MCP, it empowers agents with cross-session memory, task continuity, and personalized context. Key Features: - Personalized Context: Stores and recalls developer preferences, project guidelines, and coding patterns via semantic vector search. - Agent Daily Memory: Tracks active tasks, episodic event timelines, reminders, pending decisions, and session restart snapshots. - Self-Improving Reflection: A background worker that automatically summarizes episodic details, promotes facts, and manages memory decay. - Privacy & Governance: Complete control over user data with owner-scoped access, explicit token scopes, audit trails, and secure redaction (forget) flows. - Built for Developers: Works natively with IDE assistants (Cursor, Claude Desktop, VS Code), CLI scripts, and autonomous agent frameworks.