"An analytics platform for processing real-time data events" matching MCP servers:

Wraps multiple bug bounty platform APIs (HackerOne, Bugcrowd, etc.) behind a uniform MCP tool surface, enabling LLM agents to query programs, scope, and briefs across platforms through a single interface.

Two-way Burp Suite MCP bridge enabling AI agents to capture traffic, analyze endpoints, queue scans, and send findings back to Burp.

Connects AI assistants to the watchTowr Platform for real-time access to external attack surface data, findings, and threat intelligence. Provides 113 tools for querying and managing assets, findings, hunts, and more.

Enables Claude Code to leverage OpenAI models for expert code review, deep architecture analysis, and security audits, with automatic markdown report generation.

GDB Enhanced Features MCP server for remote debugging and CTF exploitation with 41 specialized tools including ROP search, format-string detection, and memory patching.

Enables AI assistants to search and analyze vulnerabilities and exploits from multiple intelligence sources, including NVD, CISA KEV, ExploitDB, Metasploit, and more, with tools for CVE research, exploit analysis, and report generation.

Enables AI assistants and agents to search bug bounty programs, retrieve policies and scopes, and check targets against active scope across HackerOne, YesWeHack, and Intigriti using personal API credentials.

Enables AI assistants and security analysts to inspect PCAP/PCAPNG captures through MCP tools or REST endpoints, reassembling TCP/UDP streams, extracting payload artifacts, detecting CTF flags, credential leaks and network anomalies, and generating formal PDF or Markdown forensic reports. Every finding is bound to exact frame numbers, timestamps and endpoints with SHA-256 deduplicated evidence provenance.

Enables natural language control of Cobalt Strike (version 4.8) via Claude, supporting beacon listing, command execution, payload generation, and listener management without a GUI.

Enables LLMs to perform automated penetration testing and Active Directory reconnaissance through Mythic, with tools for executing PowerShell, AD recon, domain user enumeration, and Kerberoasting.

Enables users to run synthetic support-agent evaluations, preserve and inspect traces, score results deterministically, inject failures, and enforce release review gates through MCP tools, resources, and prompts.

An MCP server that scans local codebases for quantum-vulnerable cryptography (secp256k1, Ed25519, RSA, etc.) and CI signing commands, classifying each finding as quantum-broken, post-quantum, or neither. It runs entirely locally with no network calls, providing a deterministic inventory for AI agents.

MCP server for AgentMinds collective intelligence platform, enabling AI agents to scan websites for security/SEO/performance issues, pull personalized recommendations, and share findings across the network.

Enables prioritizing vulnerabilities by real-world exploitation evidence, combining CISA KEV, EPSS, CVSS, and SSVC to rank CVEs and package versions, and providing fix recommendations.

An MCP server that lets an AI agent probe a live URL and confirm whether sensitive files (e.g., .git, .env, source maps) are genuinely served by fetching and validating the content, avoiding false positives.

An MCP server that runs curated adversarial prompts against local Ollama models to test guardrails, scoring responses with heuristic verdicts for human review.

Exports vulnerability and asset data from Rapid7 Command Platform via the Bulk Export API into a local DuckDB database, enabling AI assistants to run natural language queries and SQL analysis with sandboxed security.

Agent-native "safe to ship?" security gate for AI-generated code. Uses real parsers and inter-rocedural taint analysis (JS/TS, Python, Go) to flag the classes AI coding agents get wrong — secrets, SQL injection, SS, SSRF, path traversal, command injection, weak JWT/CORS — and ranks findings by confidence. Exposes a scan tool over MCP.

Open behavioral litmus for MCP servers — grades A–F across tool-output injection, egress, sensitive-data, and adversarial-input, with reproducible, content-addressed evidence. Tools: run_litmus, verify_attestation.

GhostHunt is an MCP server that scans your development machine for API keys, tokens, and credentials hiding in places you forgot to check.

Enables any MCP client (Claude Code, Claude Desktop, Codex, or a custom agent) to fetch the latest CVEs at query time from live sources — NVD, CVE List V5, GitHub Advisory Database, CISA KEV and FIRST EPSS — without a separate API key. Exposes search, filtering (severity, RCE, PoC, KEV, keyword, CPE), per-CVE detail and risk-scored patch prioritization as JSON tools, so answers are grounded in live data rather than model memory.

MCP server that enables Claude Code to drive the Konsulto cybersecurity audit platform from the CLI, including reading and writing findings, managing evidence, and handling scope and assets.

Connects an AI assistant to an already-running mitmweb session so it can read, search, diff, replay, and generate code from the same network flows shown in the UI.

A minimal, dependency-free MCP server that gives AI agents three real, read-only security-orchestration tools: cve_lookup, shodan_host_lookup, and nuclei_scan.

Gives AI assistants access to the Exploit Intelligence Platform for vulnerability and exploit intelligence. Supports searching CVEs, exploits, and generating pentest findings.

Enables bug bounty hunters to browse programs, inspect scope details, track scope changes over time, and receive personalized, recency-boosted program recommendations based on their skill profile.

An MCP server that audits zero-knowledge circuits (Circom, Noir, Halo2) for soundness and constraint bugs, powered by the Cysic Minimax model.

Enables interaction with NowSecure Platform for listing applications, retrieving remediation findings, and generating remediation PDFs via REST and GraphQL APIs.

Static security scanner for AI-generated (vibe-coded) Supabase and Next.js apps. Runs as an MCP server so an LLM can scan, explain findings, and produce fixes while it writes code, all locally and offline.

Scan a deployed site for an exposed Supabase service_role key and audit Row Level Security from your AI coding agent.