"A system for building an autonomous second brain" matching MCP servers:

This MCP server implements a second brain system, managing unvalidated investigation cases as JSON and validated knowledge as markdown files and skills. It provides tools for creating, updating, promoting cases, managing knowledge files, and performing semantic search.

Enables MCP clients to give AI agents a shared, persistent Graph-RAG memory backed by an Obsidian-native vault, with hybrid search, token-budgeted recall, and memory updates.

Self-maintaining personal knowledge database — MCP server with figure-level search, auto-wikilinks, and Ebbinghaus-based memory compression

A second brain in one cloud-drive folder that your AI agent reads, writes and keeps tidy.

Enables Claude to review drafts by fact-checking claims against web sources, building the strongest counter-argument, suggesting copy edits, and flagging potentially unoriginal text, returning a sourced report.

Combines structured sequential thinking with batch operation execution, enabling step-by-step reasoning with revision/branching capabilities and chained operations with variable piping between steps.

A persistent memory layer for Claude Code agents: five MCP tools (recall, remember, session, distill, status) over a markdown vault, SQLite FTS5 and an optional self-hosted Hindsight vector store, so an agent remembers facts, decisions and corrections across sessions.

Gives every AI agent you use one shared, versioned memory in a local SQLite file, so a fact saved from one app is found by all the others. Runs fully offline with no API key, records which agent wrote each change, and keeps every prior version so nothing is silently overwritten.

🐍 🏠 - Code graph for AI agents over MCP: resolves Python and TypeScript calls to fully qualified names and reports the share it could not resolve instead of guessing. Multi-hop impact analysis, authz reachability audit, architectural drift against declared patterns. Local, SQLite. uvx codegraph-brain

Turns an Obsidian vault into live, private context for Claude and other MCP clients, enabling identity-aware search, project briefings, graph traversal, recent-note retrieval, captures, and memory writes directly against the vault.

An MCP server providing steel domain knowledge to AI agents — grade properties, substitution verdicts, and mill-cert guidance as MCP tools. 23 grades, trade-verified substitution logic. Returns honest "not in knowledge base" rather than guessing.

Enables AI assistants to read and write to a personal knowledge vault of markdown notes, projects, and tasks, with tooling for search, capture, daily logs, and project management across different AI tools.

Self-hosted team bus for AI agents: cross-machine handoffs, check-in, and shared gotchas.

Search 47,000+ marketing agencies and get AI-matched with fitted agencies, from Pick an Agency.

Command a nation in SECOND STRIKE, a free real-time world war on a 3D globe: join wars, make and break pacts and launch nukes against AI nations, agents and people. A doomsday clock wipes out whoever strikes midnight. Every war is recorded and can be watched live.

Provides agents with real Linux VMs to run commands, manage files, expose ports, and track costs, all through an ephemeral sandbox service.

Enables AI coding assistants to recall past lessons, bugs, and conventions from local git history with sub-5ms latency and full privacy.

Enables AI agents to search and query local folders or public Git repositories using code-aware hybrid retrieval, returning answers grounded in exact path-and-line citations. It also provides Git history and pull-request exploration, change-impact analysis, and documentation-drift detection, with local indexing and embeddings that keep code on the user's machine.

Server that enables AI assistants to interact with Storybook design systems. Extract component HTML, analyze styles, and help with design system adoption and refactoring.

About Your task manager is the best agent memory you're not using. An agent-native context layer over your existing task app.

Enables self-hosted AI gateway and agent control plane with governed MCP tools, virtual-key budgets, caching, and audit, supporting OpenAI, Anthropic, Gemini, MCP, and A2A protocols. It provides deterministic prompt enhancement and governed execution with a zero-credential first run.

Enables users and autonomous agents to calculate dynamic price elasticity of demand and identify marginal revenue maxima through a zero-dependency, MCP-compatible Python tool.

Enables MCP-compliant clients to run a personal AI copilot with agenda tracking, autonomous sandbox execution, ambient cue handling, fast reflex actions, and episodic memory.

Enables autonomous planning by decomposing prompts into directed acyclic task graphs and resolving dependency topologies for multi-agent pipelines.

Enables MCP-compatible clients to analyze web checkout form fields and heuristically resolve buyer autofill parameters for autonomous form completion.

Enables AI agents and MCP clients to translate high-level web automation goals into deterministic, resilient browser selector action sequences. It runs as a zero-dependency Python MCP server that returns structured execution plans and telemetry for agentic workflows.

Cross-chain cryptocurrency swaps via Chainflip. Get quotes, execute swaps, and track progress. No API key required to get started.

Enables AI assistants to report what a car hire will really cost at 120+ airports in Europe, the UK and the US, drawing on measured data for second-driver fees, deposits, debit-card acceptance, desk hours and excess-cover prices. Also builds links to live prices for a person's exact dates and needs, ranked by the total they will actually pay.

Enables autonomous arbitration of refund and dispute cases by validating carrier proofs and policies through a deterministic, zero-dependency Python engine that integrates with MCP-compliant clients.

Provides a native MCP-compatible autonomous agent that decomposes general tasks, runs multi-modal web and sandbox execution loops, and self-corrects toward goal resolution using sensory and ambient inputs.