"A tool for executing code in Jupyter Notebooks" matching MCP servers:

Automates Jupyter Book notebook creation by fixing common rendering issues (bold, math, markdown formatting) and provides tools for adding cells, validating notebooks, updating TOC, and deploying.

Reviews every PR for security, bugs, and performance, then fixes the findings through MCP, keeping you in control of what merges.

Enables AI assistants to interact with Gitea repositories through intelligent tools for issue/PR management, workflow analysis, compliance checking, and content generation, plus 200+ CLI commands for complete CRUD operations.

Enables auditing codebases for production readiness, including linting, testing, CI/CD, security, branch conventions, architecture, and an A–F quality scorecard via MCP tools.

Enables coding agents to develop on one machine and verify results on another, using branch-bound runbooks, isolated checkpoints, and structured receipts to guide repair iterations.

Enables intelligent management of Google Cloud Platform infrastructure using Terragrunt, with support for experimental stacks, parallel deployments, resource visualization, cost analysis, and comprehensive infrastructure operations through natural language.

Enables coding agents and CI/CD pipelines to audit untrusted code by detecting Python sandbox escapes, SQL injection taint, ReDoS regex vulnerabilities, and CORS misconfigurations, while generating Seccomp BPF filters for safer execution.

A multi-Jenkins server management and automation tool that enables intelligent scenario mapping, multi-server management, and complete CI/CD lifecycle operations through natural language interactions.

Enables AI assistants to review GitLab merge requests by fetching changes, analyzing diffs, adding comments, and managing approvals through the GitLab API. Supports complete merge request analysis, file-specific reviews, and version comparisons.

A feature-complete TypeScript project template for rapidly developing Model Context Protocol tools with integrated best practices for code quality, testing, and automated deployment.

MCP server for IBM Code Engine — build, push, and deploy containers from Cursor, Copilot, Claude, and Cline using natural language.

Enables interaction with Azure DevOps through natural language in Cursor IDE. Supports work item management, pull requests, builds, releases, test management, and guided workflows for development teams, QA testers, and release management.

Enables AI-assisted development by running and testing code directly on Databricks clusters via natural language, then deploying Databricks Asset Bundles.

Generates VS Code installation buttons and markdown badges for MCP servers, supporting both Stable and Insiders versions with configurable inputs and commands.

Enables state-aware Terraform/OpenTofu plan evaluation against live AWS topology and CloudWatch telemetry, detecting reliability blast radius and FinOps waste before merge.

Enables automated Python code quality checks including linting, complexity analysis, typo detection, structure validation, duplicate detection, and test coverage, with integration into Cursor IDE and CLI.

A Model Context Protocol server implementation that allows AI models to interact with and manage Spinnaker deployments, pipelines, and applications through a standardized interface.

Enables automated AI-powered code review for pull requests across GitHub, GitLab, Bitbucket, and Azure DevOps via webhooks, and manual code review through MCP tools using Groq, Claude, or GPT-4.

Enables whole-program callgraph reachability analysis to locate dead functions and stale flags, and integrates with MCP-compliant clients for automated code review, breaking-change audits, and CI/CD workflow pruning.

An interactive tool that enables users to benchmark vLLM endpoints through MCP, allowing performance testing of LLM models with customizable parameters.