"Python Code for Data Analysis" matching MCP servers:

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.

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.

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.

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

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

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.

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

Multi-zone MCP server for GKE/EKS/AKS: Python tools from git, NeMo guardrails, SSO, autoscaling.

Enables AI agents to deploy static files and get live HTTPS URLs instantly, with support for custom domains and data residency.

Security auditor for CI ecosystems that detects supply-chain and injection flaws in CI/CD configuration files across seven CI platforms, providing taint analysis, remediation, and compliance scoring.

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.

An MCP server for managing Modal — apps, containers, volumes, and secrets — and for deploying & running Modal apps directly from Claude Code and other MCP clients.

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

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.

MCP server for GitLab CI/CD — pipelines, jobs, schedules, branches, tags, merge requests and repository files. Works with any GitLab (SaaS gitlab.com or self-hosted) via python-gitlab.

Enables AI clients to assess infrastructure-as-code deployment risk, generate incident response runbooks, review CI/CD delivery readiness, and calculate SLO error budgets through structured advisory tools. Runs locally over stdio without cloud credentials or external API calls, returning guidance for platform work, pull request review, and production readiness checks.

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.