"Understanding Cache for Prompt Engineering" matching MCP servers:

Enables local engineering workflow management by consolidating tickets, QA evidence, time tracking, root cause investigation, knowledge, and reporting into a single SQLite database, allowing generation of complete ticket packages for handoffs, dailies, or career evidence.

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.

An MCP server that allows agents to test and compare LLM prompts across OpenAI and Anthropic models, supporting single tests, side-by-side comparisons, and multi-turn conversations.

A local MCP server for prompt evaluation, enabling users to define test cases, run prompts against multiple LLM providers, score outputs with deterministic and LLM-graded assertions, and track quality over time, all within an AI coding environment.

Enables running deterministic multi-model prompt regression and golden dataset benchmark suites, exposing structured results and telemetry through the Model Context Protocol for integration with MCP-compliant clients.

A remote MCP server exposing 3 tools via Model Context Protocol: score_prompt (free, grades any LLM prompt A-F on 0-40 scale), optimize_prompt (returns optimized prompt + dimension breakdown, $0.025 USDC), and compare_models (Claude vs GPT-4o head-to-head, $1.25 USDC). Remote HTTP server at /api/mcp.

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).

MCP server that brings Trustabl static reliability scanning into Cursor, letting agents inventory agents/tools, flag risky patterns, score production readiness, and write SARIF reports.

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

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 checking npm packages for known security vulnerabilities using the OSV API before installation. Supports both single package checks and bulk vulnerability scanning for multiple packages at once.

A simple server implementing the Model Context Protocol (MCP) that echoes messages back, designed for testing MCP clients.

Bridges AI models with WinDbg to analyze Windows crash dumps and perform remote debugging through natural language queries, enabling execution of debugger commands and automated crash analysis.

Enables LLMs to perform browser automation tasks including web scraping, taking screenshots, and executing JavaScript using Playwright. It facilitates real-time interaction with web pages and the generation of automated test code.

Read-only MCP server that exposes the agentic-os governance, SDLC, and Quality Engineering methodology to any MCP host. It never writes to your repository and never executes code — it serves the methodology, plans an install, and verifies it, handing any commands back to the host to run.

Free MCP tools for context engineering audits of AI agents, offering on-demand analysis of system prompts, tool definitions, and context packing.

MCP server for AI security scanning. Detects prompt injections, jailbreaks, data exfiltration, and social engineering attacks in real-time using 42 regex patterns via the ClawGuard Shield API. Sub-10ms response times.

Security scanner for AI agent packages that enables AI agents to audit MCP servers and packages for vulnerabilities, prompt injection, and supply chain attacks.

Provides an isolated workspace for testing candidate code, runs tests, and returns deterministic pass/fail verdicts. Enables automated grading of software engineering solutions by ensuring reproducible test runs.

Local MCP tool for understanding JSON Schema files while planning tests, summarizing types, required fields, constraints, and generating placeholder paths without copying data.