"Python runner with streamable HTTP support" matching MCP servers:

Exposes MAID Runner validation tools (validate, snapshot, test) as MCP tools, resources, and prompts for AI agents like Claude Code and Aider, enabling seamless integration with AI development workflows.

Run the requests in a Postman collection folder and get structured, assertion-level results back straight from your AI assistant.

Enables AI agents to run, monitor, and manage Cypress tests with features like test discovery, screenshot handling, and multi-browser support.

Facilitates unified execution and result parsing for various testing frameworks, including Bats, Pytest, Flutter, Jest, and Go, through a Model Context Protocol interface.

MCP server for deterministic local test execution and normalized test result reporting, supporting pytest and Jest with coverage summaries.

Enables running multiple bash steps in one MCP call with per-step contains, notContains, or regex expectations, reporting process failures and expectation failures separately. Also provides async status polling, cancellation, guarded commands, file edits, grep, sqlite reads, and artifact retrieval.

JSON-driven E2E test runner for AI agents. Define browser tests as JSON action arrays and run them in parallel against a Chrome pool (browserless/chrome) with 28+ built-in actions, visual verification, network debugging, and flaky test detection.

Enables running YAML-defined test suites against MCP servers and agent workflows, with live or simulated execution, assertions, and regression reporting.

Validates support ticket data by checking required fields and returning a structured completeness status.

Provides a read-only MCP bridge that lets Claude Web inspect workspace files, git state, and recorded test results while Codex remains the sole executor of changes.

Enables AI-driven Python debugging with debugpy, allowing Claude Code to control breakpoints, stepping, variable inspection, and expression evaluation.

Enables AI agents to hire verified human operators for tasks requiring physical presence, human perception, or judgment, such as real-world verification, product testing, and data collection.

Enables AI coding assistants to control a persistent Chrome browser through DevTools Protocol. Supports web navigation, screenshots, element interaction, mobile testing, console logs, and network debugging in a stable Docker environment.

Enables comprehensive security vulnerability scanning and code quality analysis for Python applications. Provides detailed reports with scoring, actionable suggestions, and comparison tracking specifically designed for backend developers working with frameworks like Django, Flask, and FastAPI.

Enables analysis of AI agent traces to detect and fix failures using heuristic detectors, with no LLM calls required.

Exposes repository reconnaissance tools — file listing and verbatim reading, regex search, dependency-tree parsing across Python/Node/Rust/Go, and test-coverage ingestion — so agents can gather line-numbered evidence from real source files. This lets an audit pipeline physically re-verify every claim and drop findings that cannot be located in the code.

Enables developers to build production-grade MCP services with dependency injection, zero global state, layered imports, and a sample user-notes tool, resource, and prompt. It includes Testcontainers, Prometheus metrics, Kubernetes probes, and structured logging for testable local and production deployments.

Enables natural language interaction with Allure TestOps API for managing test cases, launches, and test plans with full CRUD support and 56+ controller endpoints.

Enables AI assistants to interact with Azure DevOps projects, including work items, test results, Git repositories, and dependency analysis via the Model Context Protocol.

Enables AI coding agents to diagnose and repair Python Selenium UI automation using real evidence from live DOM and test runs, with enforced safety boundaries and verifiable fixes.