"Gathering Information from a Company's Website Root URL" matching MCP servers:

Connects AI coding assistants to Gaffer test history and coverage data to analyze project health, debug failures, and identify untested code areas. It enables tools to track test stability, cluster failures by root cause, and monitor code coverage trends across projects.

MCP server for publishing HTML or Markdown to a live hosted URL via htmldrop. Provides tools to publish, list, and delete hosted sites, with remote OAuth and API token authentication.

Uptime monitoring your AI agent can set up: create HTTP, SSL, ping, TCP and DNS monitors checked from multiple regions, run checks on demand, read and acknowledge incidents, manage cron heartbeats, maintenance windows and status page updates.

Automates the full setup and deployment of Ballerina + Apache ActiveMQ integrations inside a WSO2 Integrator workspace, from broker validation to a runnable BI-low-code-ready project.

Local CI/CD engine for AI agents. ActionD listens to LGH git events and runs plugin jobs (Go/Python/Java/web toolchains) in isolated checkouts, with structured results, root-cause failure diagnosis, approval gates, and auto-rollback. The MCP server exposes 23 tools, including dev_cycle_run (commit → CI → structured verdict in one call) and actiond_diagnose for failed-job analysis.

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

Enables AI agents to retrieve the latest semantic version tags for container images from Docker Hub, GHCR, Quay, and other OCI registries, allowing them to pin specific versions for reproducible deployments.

Stops your AI from re-introducing bugs, leaking provider keys, or weakening tests. Bug fixes become permanent regression guards; blocked mistakes become AI lessons the agent reads and learns from before its next edit.

Deploys and manages projects on Swigs Cloud from any MCP client, turning folders or git repositories into live HTTPS sites in about two minutes.

MCP server for GitHub Actions CI failure triage. It wraps the gh CLI to fetch PR checks, failed jobs, and tail/grep failed job logs with ANSI/timestamp stripping and root-cause extraction.

Combines GitHub releases from multiple repositories into a single product release note, with context sources for LLM synthesis.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

Checks whether software dependencies (npm packages, GitHub Actions, MCP servers, Docker images) are alive, dormant, abandoned, archived, or deleted using dated observations from lastseen.dev.

Enables CI/CD failure triage for GitHub Actions by inspecting failed runs, classifying root causes with LLM assistance, retrieving relevant runbook guidance, estimating flakiness, and either preparing dry-run fix PRs or publishing structured diagnoses to Discord for human review.

Enables AI assistants to manage website deployments over SFTP/FTPS by listing sites, testing connections, comparing files, previewing changes with approval tokens, creating SHA-256 verified backups, and executing authorized uploads.

Analyzes, plans, and benchmarks LLM workload migration from x86 to Arm64, generating measurable speedups using free GitHub Arm runners.

Converts Jenkins declarative pipelines into GitHub Actions workflow YAML files, facilitating migration from Jenkins to GitHub Actions CI/CD.

Enables agents and CI pipelines to audit MCP servers and agent tool-chains by statically scanning repositories, local checkouts, tools/list exports, or live endpoints for risks such as destructive actions without confirmation, mismatched safety annotations, injection surfaces, credential or PII exposure, and unguarded command, path, or URL sinks. All checks are read-only and never execute the scanned code or call tools/call.

MCP server for Bitbucket Cloud that enables reviewing, managing, and merging pull requests, assigning reviewers by name, reading files at a ref, and inspecting pipeline status from any MCP client.

Turns JUnit XML test reports from CI into queryable QA metrics, letting AI assistants answer plain-English questions about failures, flaky tests, regressions pinned to a commit, root-cause clusters, and performance drift. Also generates ready-to-share QA health reports and self-contained HTML dashboards without logging into separate tools.