"How to browse the internet" matching MCP servers:

Screen-reader navigation cost analyzer. Measures how hard it is for assistive-technology users to discover, reach, and operate interactive targets on the web.

Semantic memory for AI builders: capture the tacit engineering know-how that never reaches your docs, recall it the moment it applies. Built in Rust on Postgres and pgvector.

Enables AI assistants to manage events, venues, organizers, and tickets on WordPress sites via CRUD operations.

SocialCrawl provides the "best" quality public web data on the internet. Access 65+ real-time social media, e-commerce and public web data from a single API with a single schema.

Enables querying of a locally pinned snapshot of the Education-to-Workforce Indicator Framework — 99 indicators and 11.8M observations — to browse questions, describe metrics, resolve places, and fetch or rank data across national, state, county, and district levels.

Just to be clear about the final JSON: the MCP server is named herdr-inbox, and it enables Claude Code agents in herdr to ask questions asynchronously without stalling, with all open questions answered from one popup and answers routed back to the asking agent.

A Model Context Protocol server that enables Claude Desktop to browse the web, likely allowing the AI to access and interact with web content.

Enables any MCP client to look up CVE intelligence — severity summaries, filtered searches, and full per-CVE detail fusing NVD, MITRE-CNA pre-NVD, CISA KEV, EPSS, and GitHub GHSA data — alongside a Shodan-derived internet-exposure footprint showing how many internet-facing services run a version mapping to a given CVE. It is free, keyless, and read-only, and distinguishes verified zeros from unassessed or failed lookups.

Exposes a headful, collaborative browser over MCP so an agent and a user can share one visible Chromium window: the agent navigates, reads, clicks, types, and fills forms, while the user watches and can take over for passwords, CAPTCHAs, 2FA, and payments, with every action written to an append-only audit trail. It also runs headless under a chat-based harness.

Give any LLM agent a real Android or iPhone. 62 MCP tools: tap, swipe, type, screenshot, screen-tree reading, app launch, camera, TTS, crash reports, batched execution. Android via ADB, iPhone via WebDriverAgent, on-device inference, Docker+KVM emulators. Works with Claude Code, Cursor, LangChain, LlamaIndex, and any MCP client. MIT.

Connects MCP clients to a local social engine that reads bounded posts, comments, replies, and feeds into local inboxes, turns source material into drafts and briefs, and publishes exact frozen jobs with recorded provider IDs and receipts across Instagram, Threads, and Substack.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

Enables local AI agents on macOS to drive the user's own signed-in Chrome through their own isolated tabs, with per-client tokens, blocked site categories, masked secrets and human hand-offs. Agents can open and navigate tabs, read pages, send real clicks and typing, inspect network requests, and hand tabs back to the user, all without touching other tabs or stealing focus.

Enables AI agents to interact with The Colony social network, including posting, commenting, voting, and direct messaging.

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

LLM Optimizer is an AI visibility intelligence platform. It analyzes how large language models and AI search engines perceive, cite, and recommend brands; then provides research-backed optimization strategies to improve that visibility.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.