"Information on agent swarm architecture" matching MCP servers:

Enables ChatGPT on Windows to securely access codebases, Git, terminals, language servers, debuggers, SQLite, local HTTP services, adaptive project memory, engineering skills, checkpoints, audit logs, and sandboxed command execution through MCP tools.

A local MCP server that lets AI agents send desktop notifications during tasks, ask for attention, and report completion. Supports macOS, Linux, and Windows, with optional webhook delivery.

Local, searchable project memory for AI coding agents. Markdown source of truth, MCP interface, safe structured updates

Enables AI agents and MCP clients to drive real Chrome, Edge, Brave, and Firefox browsers through native automation protocols. It exposes tools for navigation, page snapshots, form filling, clicks, screenshots, console/network inspection, and tab management.

MCP server that keeps language server sessions warm and routes multiple languages through one process. Agents get persistent cross-file awareness, speculative execution (simulate edits before writing to disk), and 20 skills that encode correct multi-step operations like safe rename, blast-radius analysis, and end-to-end refactoring. Single Go binary, no runtime dependencies.

Universal session & handoff layer for AI coding agents: shared task, decision, blocker and test state, checkpoints, and deterministic handoff between Claude Code, Codex, OpenCode, Cursor, Cline, pi and omp. Local SQLite, git-aware, 25 tools + 7 resources over stdio or streamable-http.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

Provides git-aware safe file operations, allowing AI agents to delete files while protecting git-tracked source code from accidental deletion.

Exposes three read-only tools that list local Agent and MCP runtime candidates, inspect running processes, and check a live runtime against an approved artifact expectation. Returns explicit verdicts such as matched, stale, leaked, conflict, unknown, or not running without exposing argv, environment values, or file contents.

MCP Guard Server - Budget control, approval workflows and audit logging for AI agents (Claude Code, Cursor, ChatGPT)

Enables coding agents to coordinate in spaces through direct messages, topics, blackboards, and durable inboxes, exposing bus tools over MCP and RPC while pushing inbox updates over SSE.

A powerful MCP tool for forging and managing AI agent personalities with customizable expert traits and behaviors.

A memory, docs, and repo context layer for engineering agents, providing typed persistent memory, RAG document retrieval, and repository-aware tools to recall decisions, runbooks, and operational knowledge across sessions.

Enables agents to capture task-scoped residue evidence during test and build workflows, exposing files, directories, processes, and ports for user-authorized cleanup and post-cleanup verification without deleting or modifying anything itself.

Enables AI assistants to search PatchXNote records, inspect AI-generated results, create Markdown drafts, and send user-approved messages to webhooks while keeping server data read-only.

Provides persistent, searchable memory across AI coding agent and chat history (Claude Code, Codex, Gemini CLI, ChatGPT, and more) via retrieval-augmented generation, enabling semantic and hybrid search to retain context across sessions.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

Trust layer for AI agents: check any x402 endpoint or MCP server before paying (live status, real price, payTo vs listing, OFAC), plus agent passports with two-sided signed receipts and signed skill releases. Every result is a signed event in a public transparency log.

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

See which AI agent and MCP server talks to what on Windows, with a rule-based security verdict

MCP server for gavel, a code-quality gate for Bazel monorepos. Exposes judge, findings, coverage and architecture tools so a coding agent can check its change against the quality gate (lint, coverage, layer rules) before declaring it done — all off the Bazel build graph, over just what changed.

A high-performance Go-based MCP server that provides a microservice architecture for orchestrating diverse tools through gRPC and HTTP/REST APIs. Enables seamless integration of language-agnostic tools including ML capabilities, web search, calculations, and human interaction for intelligent agent workflows.

Enables governed multi-agent software factory operations through eight typed, audited tools for Sprint planning, submission, decisions, inspection, cancellation, and resumption.

Enables an agent to query connected MySQL databases through named aliases, running reads immediately while holding data-changing statements for local user approval. Passwords are entered on the user's own computer and never reach the agent.

Provides AI assistants with persistent graph database memory using Neo4j, enabling task management, relationship understanding, semantic search with embeddings, file indexing, and multi-agent coordination through the Model Context Protocol.

https://gopls-mcp.org/ MCP server for golang projects development: Expand AI Code Agent ability boundary to have a semantic understanding and determinisic information for golang projects.

An MCP server for inter-session communication between Claude Code instances. Built on NATS JetStream, it provides room-based messaging, direct agent communication, presence tracking, and message history.

Enables administering Ubuntu hosts through MCP over stdio, currently exposing a read-only get_system_info tool that reports OS release, hostname, kernel, architecture, memory, and root filesystem capacity.

You are capable of interpreting golang code and judge it under the most strict ddd and clean architecture paragidms

Enables LLM agents to play six-on-six Pokémon battles with hidden information, a full move set, and a deterministic battle engine, including local play, reproducible matches, and benchmarking against AI opponents.