"Information about Threat Intelligence" matching MCP servers:

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

Enables AI agents to capture screenshots, scrape structured page content, and run automated visual checks on live websites via a headless Chromium browser.

A Model Context Protocol server providing security vulnerability intelligence tools including CVE lookup, EPSS scoring, CVSS calculation, exploit detection, and Python package vulnerability checking.

Enables recurring website maintenance checks — domain registration, SSL certificates, uptime, technical SEO and accessibility — across a portfolio of client sites, using publicly available information. Results are returned as structured data and can be sorted by urgency into a client-ready report.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

An MCP server that gives AI assistants the ability to check open-source packages for vulnerabilities, enrich findings with real-world exploit intelligence, and statically analyse whether vulnerable code is actually reachable in your project.

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.

An MCP server that turns Zod schemas into mocks, violations, and contract tests, enabling AI agents to reason about API contracts without manually crafting payloads.

Automated Playwright E2E test repair powered by a self-improving, governed MCP server that runs failing tests, collects failure artifacts, reasons about root causes, validates and applies fixes, and re-runs to verify.

Enables AI assistants to access RenderDoc capture data and assist with graphics debugging by retrieving draw calls, shader information, buffers, textures, and pipeline state.

Test coverage intelligence MCP server for TypeScript and JavaScript projects. Analyzes coverage reports to find untested functions, computes cyclomatic complexity, and suggests test cases locally.

Behavioral verification intelligence for AI coding agents. Reads a TypeScript or JavaScript repo, clusters functions into 25 semantic workflows (Authentication, Payments, Webhooks, Caching, Queue, and more), and emits concrete adversarial probes per workflow. 17 MCP tools, local SQLite state, zero cloud.

MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.

Enables coding agents to examine a rendered web page by cross-checking what the browser reports about the DOM/CSS against what is actually painted in pixels, pinpointing which element is broken and the evidence why. Reports visual problems without requiring a baseline, and is designed to grow into design-token extraction and accessibility/UX audits built on Playwright.

Allows LLM agents to query Playwright JSON test results for run summaries and failure information.

Enables natural-language validation of IFC models against Türkiye's building-permit BIM regulation, including compliance checks, EK-9 form generation, and queries about required classes and properties.

Local MCP server for querying VCD waveform files via SQLite; enables AI agents to ask precise questions about signal values, transitions, and clock cycles without dumping raw VCD text.

A real security scanning MCP server for medical information systems, supporting port scanning, vulnerability detection, medical system identification, and compliance report generation.

Enables defect management on the SEPP platform, allowing users to query defects, retrieve user project information, and set up automated monitoring with alerts for new or unresolved defects via webhook or email.

Enables AI assistants to analyze and inspect RenderDoc capture files, providing tools to examine draw calls, textures, buffers, and shader information. It allows developers to perform graphics debugging and resource analysis through natural language interactions.