"How to find people on Facebook" matching MCP servers:

Helps support engineers find similar GitHub issues to speed up troubleshooting by searching repositories and calculating similarity scores based on issue descriptions.

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.

Enables AI coding agents to orchestrate the full software development lifecycle on GitHub, including planning, issue creation, code review, security triage, and release readiness checks.

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

Enables LLMs to list, create, and comment on GitHub issues using your own GitHub identity via stdio transport.

Enables developers to find personalized open-source contributions by analyzing GitHub profiles and matching them with relevant 'good first issues' and beginner-friendly repositories. Provides comprehensive contribution tooling including repository health scoring, setup difficulty assessment, impact estimation, and automated PR planning.

Connects Claude to on-premise TFS (Team Foundation Server) via REST API, enabling read/write of work items, backlogs, sprints, and pull requests without manual copy-paste.

A graph-native code repository for people and AI agents. Kin helps you and your AI agents understand what a code change might affect before you make it.

Enables AI assistants to interact with Azure DevOps Server 2022 through natural language, enabling queries and operations on pipelines, pull requests, builds, and repositories.

Enables AI assistants to access and analyze GitHub profile data, providing insights on repositories, commit history, coding patterns, and generating portfolio summaries for developers and recruiters.

Enables an MCP-capable assistant to review GitLab merge requests, GitHub pull requests, and pre-push local branches against ticket requirements, domain rubrics, compiler/lint pre-passes, blast-radius analysis, and SonarQube findings, and to draft inline comments or follow-up tasks in the user's chosen language. Nothing reaches a remote host until a human approves the exact content in an interactive terminal.

An intelligent code version management system based on the MCP protocol, providing AI-enhanced code snapshots, project versioning, and smart change detection for AI-assisted programming.

Enables pull-request workflows on GitHub and Bitbucket Cloud, providing tools to list, create, update, merge, close, comment on, and review pull requests via a unified interface.

MCP server for Bitbucket Cloud, focused on pull-request review workflows

An MCP server that enables AI clients to autonomously discover, analyze, fix, test, submit, and track open-source contributions on GitHub through specialized tools.

Enables fetching Pull Request diffs from GitHub and posting inline review comments directly on PRs, either as pending drafts or published immediately.

Makes git commits on behalf of AI by appending "(aider)" to the committer's name, allowing tracking of AI contributions in your codebase.

An MCP server that enables AI agents to retrieve detailed GitHub Pull Request information using git commit hashes, branch names, or PR numbers. It automatically detects repositories and extracts comprehensive PR data including descriptions, labels, and reviews via the GitHub CLI.

Provides natural language search capabilities for Spryker GitHub repositories, packages, and public documentation. It enables code-level searches across Spryker organizations to help developers find relevant modules and technical information.

Enables AI agents to create GitHub issues on explicitly allow-listed repositories using GitHub App authentication, with input validation, rate limiting, and no token exposure.