"MCP server for remembering key project decisions, tracking framework and settings" matching MCP servers:

Human-in-the-loop approval: a person approves the exact AI action with a passkey, and any service can check the signed approval offline.

Enables MCP clients to perform local, typed decision workflows with Ollama and Tev1, including classification/scoring, request routing, preflight checks, workflow execution, and health checks through five read-only tools.

Enables coding agents to send natural language and JSON application state to a judge tool that returns typed judgments (yes/no probability, choice, or score) with model and usage metadata, and fails gracefully without blocking workflows.

A communication framework that enables multiple AI agents to collaborate through asynchronous messaging, agent registration, and discovery. It facilitates complex task coordination and real-time discussion across distributed agent workflows using the Model Context Protocol.

A middleware system that connects large language models (LLMs) with various tool services through an OpenAI-compatible API, enabling enhanced AI assistant capabilities with features like file operations, web browsing, and database management.

Facilitates AI session handoffs and next steps tracking through project-based organization, supporting task prioritization and seamless workflow management.

AI-native orchestration layer with 80+ tools for task management, code editing, browser automation, terminal control, and persistent memory across CLI, local MCP, and cloud deployments.

Enables coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.

Coordinate AI agents and people in shared rooms: post, claim and hand off work, close with receipts.

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

MCP server that enables AI assistants to run multi-step agent pipelines (e.g., Issue Analyst → Code Writer → Test Runner → PR Opener) from conversations, with support for Devin, shell, Python, and HTTP agents.

Enables AI project feasibility evaluation and solution planning through structured reasoning stages, using Gemini API and web search for up-to-date research.

Enables AI agents to break down complex tasks into manageable pieces using a structured JSON format with task tracking, context preservation, and progress monitoring capabilities.

An MCP server that transforms text into knowledge graphs and autonomously generates insights by combining Montague Grammar with Zettelkasten methodology.

Maximizes AI agent context window by enabling compact code reading and editing, reducing tokens by 40% for deeper codebase understanding.

An AI-centric MCP server that enables automated Xilinx Vivado workflows, including project management, synthesis, implementation, and timing analysis. It allows AI agents to drive hardware design processes while integrating directly with the official Vivado GUI for visual context.

Enables AI agents and project managers to turn GitHub Projects v2 into an autonomous project-management layer, handling task checkout, atomic claims, heartbeats, reviews, token budgets, and AI-powered PRD, sprint, triage, and roadmap workflows. It also provides GitHub Actions, Releases, Branches, MCP Resources, and MCP Prompts support with GitHub-native storage.

Enables AI agents and human managers to orchestrate GitHub-based project work by self-assigning tasks, coordinating swarms, tracking token budgets, generating PRDs, planning sprints, triaging issues, and materializing tasks as GitHub issues, milestones, and project views.

Turns GitHub Projects v2 into an agentic project management layer where AI coding agents self-assign tasks, coordinate via heartbeats, submit work for review, and stay within token budgets. It also provides AI-powered PRD generation, sprint planning, issue triage, and a PRD-to-issues pipeline, all backed by GitHub-native storage with no external infrastructure.

Enables a small local LLM to build and maintain a project map by offloading event ID/date generation, batching, synchronization, recovery, and finalization to the server, so the model only decides what to read and how to classify.

Turns GitHub Projects v2 into an agentic project-management layer where AI coding agents can register, claim tasks, coordinate, submit work for review, and enforce token budgets, while humans can generate PRDs, plan sprints, triage issues, and create roadmaps using GitHub-native storage.

Turns GitHub Projects v2 into an agentic project-management layer where AI coding agents register, atomically claim and self-assign tasks, heartbeat, review each other's work products, and operate within token budgets. It also exposes 20 compound tools (169 actions) plus MCP resources, prompts, and sampling for AI-powered PRD generation, task breakdown, sprint planning, issue triage, and a PRD-to-issues pipeline, all stored natively in GitHub with no external infrastructure.

Enables AI coding agents and project managers to turn GitHub Projects v2 into an autonomous project management layer with task checkout, multi-agent swarms, PRD-to-issue pipelines, sprint planning, triage, and GitHub Actions/Releases/Branches support via 20 compound tools and 169 actions.

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

Enables AI agents and project managers to turn GitHub Projects v2 into an autonomous project management platform with task checkout, multi-agent coordination, PRD-to-issue pipelines, sprint planning, triage, and GitHub-native storage.

Enables AI coding agents and teams to turn GitHub Projects v2 into an autonomous task substrate for registering, claiming, tracking, reviewing, and completing work, while also supporting AI-powered PRD generation, sprint planning, issue triage, roadmap creation, GitHub Actions/releases/branches, and MCP resources/prompts.

Enables AI agents to explore, search, and reference code implementation details across different project repositories. It also supports a task delegation protocol for agents to request and track work between separate codebases.

Turns GitHub Projects v2 into an agentic project-management platform where AI coding agents can register, claim and track tasks, coordinate via heartbeats, review work, and stay within token budgets. It also enables AI-powered PRD generation, sprint planning, issue triage, roadmap creation, and PRD-to-issue materialization using GitHub-native storage.

Turns GitHub Projects v2 into an agentic project-management substrate where AI coding agents register, atomically claim tasks, send heartbeats, review each other's work, and stay within token budgets, while also offering AI-powered PRD generation, task breakdown, sprint planning, and triage. Everything is stored in GitHub issues, project fields, and comments, so no external database or infrastructure is required.

Turns GitHub Projects v2 into an agentic task backbone where AI coding agents register, atomically claim and track work, heartbeat, review each other, and stay within token budgets, while humans get AI-assisted PRD generation, sprint planning, triage, and PRD-to-issue pipelines. All state is stored natively in GitHub issues, project fields, and comments, so no external infrastructure is required.