"Information about Shodan, a search engine for internet-connected devices" matching MCP servers:

An MCP server that provides information about Utkarsh, including bio, skills, work experience, and portfolio projects, accessible via local stdio or remote HTTP with OAuth.

Exposes a connected-vehicle OBD-II/telematics platform to AI agents, enabling vehicle health scoring, live data queries, DTC decoding, maintenance prediction, and gated remote commands via a pluggable data layer.

A minimal MCP server for Shodan, enabling infrastructure reconnaissance through tools like host lookup, search, CVE lookup, and DNS queries.

Enables searching Shodan for devices and services, and looking up host details, through natural language.

Enables searching documentation from GitHub repositories and web pages via MCP tools, with in-memory indexing and caching for fast retrieval.

Provides telemetry, rule-based anomaly detection, and maintenance recommendations for a synthetic connected-vehicle fleet through five narrow MCP tools, enabling fleet monitoring and analysis without external APIs.

Enables comprehensive security reconnaissance, vulnerability assessment, and threat intelligence gathering by integrating Shodan's API. It provides tools for searching internet-connected devices, performing DNS operations, and querying the Shodan exploit database.

MCP server for querying the Shodan API and Shodan CVEDB. This server provides tools for IP lookups, device searches, DNS lookups, vulnerability queries, CPE lookups, and more.

Enables AI agents to perform passive reconnaissance, vulnerability intelligence, DNS analysis, and device search using Shodan's database of internet-connected devices, all from within your IDE.

MCP server that exposes Shodan API for host information, search, alerts, scanning, and exploit data.

Provides AI agents with 12 real-time tools for search synthesis, grounded research dossiers, claim verification, citation generation, sentiment analysis, expert ranking, timeline creation, and competitive landscape mapping over Streamable HTTP or stdio.

An MCP (Model Context Protocol) server that gives AI agents live, structured ad intelligence across Facebook, Google, and Instagram — data that no base model can produce from training alone. Powered by Apify actors. Works with any MCP-compatible client: Cursor, Claude, etc.

Enables coding agents to retrieve exact RFC section text, status and obsoleted-by chains, errata, and IANA registry values such as HTTP status codes, media types, and port numbers.

Enables MCP clients such as Claude, Cursor, and VS Code Copilot to search more than 50,000 internet radio stations by name, genre, country, or language and retrieve their direct stream URLs. It also surfaces the most popular and trending stations along with country and genre listings, with no API key or account required.

Enables AI-driven creation of savable Wallpaper Engine web wallpaper source projects through a local STDIO service, with bounded project discovery, revision-checked file edits, checkpoint restore, validation, and publish staging. It also opens managed preview windows to set public properties and capture screenshots and bounded logs as runtime feedback from the official engine.

Enables MCP clients to perform intelligent web searches via Traylinx's Agentic Search API, returning text summaries and structured data like HTML, images, and news.

A Model Context Protocol server that provides web search capabilities using Google Custom Search API and webpage content extraction functionality.

Exposes self-owned AI agent plugins as tools to Claude Code via a stdio MCP server, allowing Claude to call system info, calculator, or scribe transcriber directly.

Provides access to Shodan's IoT search engine API for device search, host intelligence, exploit database, network scanning, and security analysis through natural language.

The MCP Internet Speed Test is a specialized Model Context Protocol (MCP) server that enables AI models and agents to perform internet speed measurements.

Gives MCP clients host recon, banner search, DNS lookups, CVE and exploit data, on-demand scans, and network alerts, with compact summaries and credit/scan safeguards plus keyless InternetDB and CVEDB access.

Enables grounded, dated research from a curated AI-knowledge repository, offering free BM25 retrieval and a research loop that combines the knowledge base, curated sources, and the open web with tiered citations.

Eyes for AI coding agents: deterministic MCP server that tells the LLM which CSS rule wins, in which file, on which line — and why. Cascade verdicts, blast radius, interaction timelines, pixel-perfect audits.

Local MCP tools to create, preview, inspect, verify, and compile PDF documents from HTML/CSS and structured data with Fullbleed. Python/Rust engine, MIT licensed, with document paths confined to a chosen workspace.

attendance-engine-mcp is a Model Context Protocol server that gives AI agents deterministic, fixture-backed tools for workforce attendance and wage-and-hour compliance. Built on @attendance-engine/core — a pure-function, zero-deps, 100%-covered TypeScript engine — it lets Claude, Cursor, Windsurf, or any MCP host correctly answer the questions HR/payroll teams actually ask: did this person clock i

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

MCP server (stdio, official Python SDK) exposing a single tool: quantity_execute. Performs dimensional calculation and unit conversion with evidence. Solves silent unit errors in LLM agent chains: 20 m/s × 3 h returns 216 km.

Enables programming agents to capture errors and conversation signals, reflect on root causes, consolidate reusable skills, and retrieve relevant context for future tasks, providing a self-learning memory loop.