"Information about Python programming or Python-related topics" matching MCP servers:

A DevOps-friendly template for building MCP servers with CI/CD, Docker support, and automatic documentation generation using fastmcp and FastAPI.

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.

Enables managing SAP Cloud Integration iFlows, packages, configurations, deployments, and runtime artifacts via natural language through the OData API.

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.

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.

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 coding tools to validate local projects and publish or update applications to Bedengan App Hub via API, with automated checks and live deployment without manual approval.

Enables AI coding agents and CI to vet npm dependencies before they reach the lockfile, flagging hallucinated, slopsquatted, or otherwise risky packages with evidence-backed verdicts.

An MCP server that enforces disciplined Git workflows by planning coding tasks into small, test-gated commits, ensuring tests pass before staging and preventing out-of-scope or oversized changes.

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 inspection and management of Apache Airflow DAGs, runs, and logs across multiple instances. It provides tools for monitoring workflows and performing gated write operations like triggering DAGs or clearing task instances.

Post-quantum readiness for AI coding agents: scans code for quantum-vulnerable cryptography (RSA/ECDH/ECDSA/DH) and returns NIST ML-KEM/ML-DSA/SLH-DSA (and hybrid) migration guidance, with fix verification and dependency checks. Content-based, advisory tools only; runs local (npx @quantakrypto/mcp) or as a hosted OAuth endpoint.

MCP server for DCI API enabling AI models to extract and analyze DCI jobs, components, and topics, with integrations for Google Drive, Jira, GitHub, and Red Hat support cases.

Enables management of Railway.app infrastructure through natural language, including deploying services from GitHub repos or Docker images, managing environment variables, monitoring deployments, and configuring networking and volumes.

Python MCP server for GitHub operations, providing native tool integration with Claude Code and other MCP clients. It enables managing issues, pull requests, CI status, milestones, and batch operations via natural language.

MCP server for agent-controlled deployment on Pier, exposing 14 tools mapped to Pier REST API endpoints for managing deployments via stdio or HTTP.

Enables retrieving comprehensive information about GitLab merge requests, including details, commits, diffs, discussions, approvals, and CI/CD status, through natural language.

A Python MCP server that wraps the GitHub CLI (gh) to provide structured tools for searching, reading, and managing GitHub resources like repos, issues, PRs, releases, and workflows, with write commands disabled by default.

Enables AI assistants to manage and operate Venelx CI/CD pipelines through the Venelx REST API, including listing projects, triggering builds, checking status, tailing logs, accessing artifacts and workers, and diagnosing signing or GitHub access.

Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.