"Replicating ChatGPT Memory Functionality for Task Management and Skill Tracking" matching MCP servers:

Provides a hand-written MCP server integrated with a LangGraph agent to benchmark multiple agent topologies and measure their failure rates.

Enables ChatGPT to control Mobile Safari on a real USB-connected iPhone through an OpenAI Secure MCP Tunnel and Appium MCP, including device selection, WDA preparation, and Safari session automation.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables Claude to independently check an AI agent's work in a local git repository by recording a baseline, running only the checks relevant to the changed files, and cross-checking what the agent claimed. Returns a compact, evidence-labelled report with a verdict and a ready-to-paste next prompt, so unverified success claims can't slip through.

Enables coding agents to plan, reference, and gate UI work through preflight briefs, design-intent dispatch, and a static review checklist.

Gives any agent eyes and ears by indexing video, audio, and screen activity into searchable, timestamped frames, transcripts, and OCR text that answers can cite. It also evaluates frozen verification contracts over file digests, JSON values, SQL results, HTTP responses, and DOM state to return a deterministic verdict from a separate process.

An MCP server that gives AI agents eyes and hands to visually interact with running apps across 10 platforms, enabling natural language-driven testing and automation without test code.

Exposes read-only and write probes over a remote Streamable-HTTP /mcp endpoint so users can verify that ChatGPT Web discovers and calls remote tools, and test whether write actions actually execute on their plan. Real HWPX document editing is not yet supported; this P0 release only measures connectivity and read/write action boundaries.

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.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

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).

Scaffolds new MCP servers for the OpenSIN-Code ecosystem with templates for Python, Node, Go; provides tools to add tools, test, validate, register, publish, and audit servers.

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.

Enables autonomous web agents to prune DOM trees, canonicalize URLs, auto-map form inputs, and convert HTML/Markdown tables into typed JSON, then score execution trajectories with action-level precision, recall, and Levenshtein edit distance against gold benchmarks. Runs as a zero-dependency, stdlib-only MCP server over JSON-RPC 2.0 stdio for Claude Desktop, Cursor, and Windsurf.

Provides an MCP-compatible profiler for measuring edge and on-device LLM inference latency, including TTFT, TPOT, tokens-per-second throughput, and P50/P90/P99 jitter distributions. It enables agents and developers to run these telemetry benchmarks from MCP clients such as Claude Desktop, Cursor, and Windsurf.

Enables users to simulate quantum circuits, evolve qubit state vectors, generate EPR Bell pairs, perform Born-rule measurement, and run Grover amplitude amplification through a zero-dependency MCP interface.

A multi-task MCP server for AI code optimization and testing that integrates with VSCode/Cursor, CLI tools, and autonomous agents. It provides tools for running tests, generating code improvements, and managing objectives across different environments.

Validates .mcp.json and claude_desktop_config files to catch duplicate server names, missing binaries, invalid environment variables, and unreachable endpoints. Reports each problem along with how to fix it, so silently failing MCP servers can be diagnosed and repaired.