"Resources for Learning Law" matching MCP servers:

Minimal MCP server for testing client support for resources/subscribe and notifications/resources/updated, simulating an asynchronous status update.

A raw TypeScript MCP server simulating incident lifecycles with tools, resources, prompts, and signed multi-round remediation approval. Enables CLI-based interaction, protocol negotiation, and health/security testing without external MCP SDK dependencies.

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.

Enables LLM agents to programmatically inspect, debug, and test other MCP servers by wrapping the MCP Inspector CLI. Supports listing and calling tools, reading resources, and testing prompts on both local and remote MCP servers.

Enables testing how different MCP clients handle tool results containing both content and structuredContent, including outputSchema enforcement and mismatch scenarios.

A high-performance, multi-protocol API mock server with 18 built-in MCP tools for managing HTTP, GraphQL, gRPC, WebSocket, MQTT, SSE, and SOAP mocks, chaos engineering, stateful CRUD resources, and request verification.

Connects AI coding assistants to TestCollab for managing test cases, plans, and suites directly through natural language. It enables users to create, update, and query testing resources within integrated development environments and AI chat clients.

A reference MCP server that enforces identity-aware tool access using role-based permissions (Reader, Analyst, Auditor) and supports multiple identity routes like Entra RBAC, Copilot Studio, and Okta XAA. It provides read-only access to test evidence with explicit denial paths for unauthorized tools.

Enables prompt optimization loops and regression test suites for Claude Code, with a companion web UI for real-time visualization of scores and prompt revisions.

A research harness for measuring whether MCP clients preserve informed consent when tool definitions change after approval.

Evidence-driven quality testing for Model Context Protocol servers. It connects as a real client, captures the complete model-facing contract, identifies agent-usability risks, measures context cost, and executes only explicitly approved target calls.

Zero-sidecar MCP server embedded directly in the Godot 4 editor as a GDScript EditorPlugin — no Node or Python sidecar. Reflection-driven tools over any engine class, validate-before-write GDScript, scene/script/resource authoring, and a runtime play-test loop (play, screenshot, input, assert).

A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.

CodeSafer is an MCP server that scans AI-generated code for 9 categories of hidden security threats — including invisible Unicode, Trojan Source, homoglyphs, and rules file backdoors — using static analysis plus CodeBERT deep learning. Runs locally, free tier available.

Small diagnostic MCP server for checking what MCP clients actually support, including tools, resources, prompts, and transport behavior.

Enables automated migration of JavaScript/TypeScript packages from legacy test runners like Karma, Webpack, and Mocha to Vitest, including AST codemods, multi-project test scaffolding, and self-learning diagnostics for resolving runtime errors.

Website testing MCP server built for AI agents: 63 Playwright tools with hard assertions, auto form-fill, persistent authenticated sessions, network mocking, and accessibility/SEO/GEO + Lighthouse audits.

Provides seamless access to the Fake Store API for AI assistants with 18 CRUD tools for managing e-commerce data including products, carts, and users. Perfect for e-commerce demos, testing, and learning MCP development with zero configuration required.

Enables evaluating Python projects against a deterministic quality bar using analyzer findings, per-dimension scores, and a review verdict, exposed through MCP tools, resources, and prompts. Users can run evaluations, inspect rules, and list analyzers to assess code readiness for review.

Enables AI assistants to manage Apple App Store Connect resources like apps, builds, TestFlight, and reviews through natural language.