"MCP Server Growth Analysis" matching MCP servers:

A continuous integration server that automates Python code analysis, providing linting and static type checking tools for quality assurance.

Provides AI assistants with access to Capawesome documentation and Cloud management tools, enabling app, build, and deployment operations via API token authentication.

Checks npm and PyPI packages for outdated versions, deprecation status, and breaking changes with cited sources, enabling AI agents to verify dependency freshness.

Model Context Protocol (MCP) server for GitLab — exposes 1006 GitLab REST & GraphQL API operations as MCP tools (42 meta-tools / 57 enterprise), 24 resources, 38 prompts, and 17 completion types for AI assistants. Written in Go, single static binary, stdio and HTTP transport.

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.

Enables AI coding tools to scaffold, build, and deploy Node.js or Python apps to Varity with a single command, automatically provisioning databases and backend services.

A Model Context Protocol server for checking package versions and metadata across multiple package managers including npm, PyPI, Maven, NuGet, RubyGems, Crates.io, and Go modules.

MCP server providing tools to verify task completion (tests pass and changes match claim) and confirm bug reproduction before fixing.

Enables AI assistants to summarize Playwright test runs, identify flaky tests, heuristically triage failures, generate Playwright test stubs, and retrieve recent GitHub CI status through MCP tool calls.

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.

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

A Model Context Protocol server that enables AI assistants to interact with Azure DevOps services, allowing users to query work items with plans to support creating/updating items, managing pipelines, handling pull requests, and administering sprints and branch policies.

An MCP server that gives AI agents full access to the Harness.io platform through 11 consolidated tools and 207 resource types.

MCP server for MongoDB's Evergreen CI/CD platform, enabling AI assistants to manage projects, analyze builds, and troubleshoot failures via natural language.

Provides AI agents full access to the Harness.io platform through 10 consolidated tools and 137 resource types, enabling management of CI/CD, GitOps, Feature Flags, cost data, and more.

Enables deployment and management of applications on the Citizen platform from git repositories or local files. It allows AI assistants to monitor deployment logs, handle authentication, and automatically fix build errors using intelligent error analysis.

MCP server for Xcode build optimization, providing build-timing analysis, DerivedData/module-cache management, and build-speed config linting for AI coding agents.

A CircleCI MCP server specialized in config review, providing tools for fetching actual configs, validating changes, and analyzing workflow bottlenecks, health, and DAG dependencies.

Production-ready, zero-knowledge AI DevOps deployment, infrastructure provisioning, self-healing execution, and persistent documentation engine for VPS servers.

Enables interaction with Rundeck instances to list jobs, execute jobs with options, and retrieve execution status and logs through natural language commands.