"Information About Persistent Memory" matching MCP servers:

Enables Playwright QA automation with a custom Memory MCP that provides tiered retention and mortgage compliance audit, allowing AI agents to remember flakiness history and journey maps while adhering to data privacy and audit requirements.

Persistent visual cache for LLM-driven software development. Caches screenshots using perceptual hashing, vector search, and AX trees to prevent token overhead and visual hallucination loops.

Enables deterministic behavioral regression review of pull-request diffs by checking changed code against approved incident-derived contracts. It exposes read-only tools to assess diffs and pull requests, list and fetch contracts, and report outcomes over stdio without mutating repositories.

Enables AI assistants to evaluate task complexity, verify package installation safety, and log complex reasoning processes. Provides structured assessment of multi-step tasks and environment validation for safer code execution workflows.

Provides an in-memory notebook interface with tools to create, retrieve, list, update, and delete notes, designed to test agent behavior through realistic MCP tool interactions.

An MCP server that reads test reports and provides regression analysis tools for comparing runs, identifying regressions, fixes, and persistent failures.

An MCP server that enables AI assistants to visually inspect and interact with rendered web pages via a persistent headless Chromium browser, supporting navigation, screenshots, clicks, viewport resizing, and console log retrieval.

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

MCP server exposing a "run" tool that benchmarks agent-memory backends and returns a reproducible JSON report.

Enables coding agents to drive and inspect running Shiny apps (R and Python) by setting inputs, clicking, reading outputs, and getting detailed error information.

An MCP server that turns Zod schemas into mocks, violations, and contract tests, enabling AI agents to reason about API contracts without manually crafting payloads.

Automated Playwright E2E test repair powered by a self-improving, governed MCP server that runs failing tests, collects failure artifacts, reasons about root causes, validates and applies fixes, and re-runs to verify.

Enables AI assistants to access RenderDoc capture data and assist with graphics debugging by retrieving draw calls, shader information, buffers, textures, and pipeline state.

Enables AI agents to rapidly drive and inspect real web pages through persistent browser sessions, using accessibility-tree snapshots and DevTools-grade diagnostics to identify and diagnose issues.

.FAF (Foundational AI-context Format) with 50+ tools - Only Persistent project context that integrates seamlessly with Claude Desktop workflows. Officially merged (#2759) Anthropic MCP server.

MCP server that generates ISO/IEC/IEEE 29119-3 compliant test plan drafts from project information, providing a structured resource for the standard's test plan outline and a tool for draft creation.

This MCP server enables AI agents to perform exploratory UI testing of live web apps by providing a structured view of page elements, deterministic correctness oracles, enforced read-only or opt-in write policies, persistent cross-run memory, and gap-checked coverage reports.

Allows LLM agents to query Playwright JSON test results for run summaries and failure information.

Provides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.

Enables natural-language validation of IFC models against Türkiye's building-permit BIM regulation, including compliance checks, EK-9 form generation, and queries about required classes and properties.