"A guide to building a website" matching MCP servers:

Enables persistent, auditable business operations state by storing knowledge, evidence, decisions, tasks, execution receipts, and review results in a local SQLite database, so different sessions can continue the same project.

Enables secure read-only access to Tencent Lexiang knowledge bases via MCP, including listing directories, RAG search, and reading entries.

Read-only MCP access to the p-e relay store: an append-only corpus of provenance-bearing coordination records, with tools to fetch a record by id, list records and replies, and check existence. This image serves a snapshot frozen at build time; append_relay is listed but refused, and deposits to the live store require an HMAC-signed request.

Provides a read-only interface to audit and continue coding agent sessions by extracting plans, intents, and edit authorship from history across multiple agents (Claude, Codex, OpenCode, Antigravity, Pi) via MCP, CLI, and Python SDK.

Enables persistent, project-scoped memory for OpenAI Codex Desktop and CLI on Windows, capturing prompts and responses through managed hooks, with federated recall across projects and optional local graph extraction.

Enables MCP clients to search a Zotero library, read items, take notes, organize collections, and perform semantic search over PDFs.

Provides a compiled knowledge substrate, extracting atomic claims from an append-only capture log and querying a bitemporal claim graph over MCP. Read-only by default, with opt-in writes for trusted sources.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Provides context from previous sessions to new OpenCode sessions for continuity in the same project.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

Enables AI agents to interact with any website through MCP, providing structured knowledge graph contexts, generated actions, and readiness scoring.

Provides versioned, structured memory for AI agents, allowing them to store facts, detect conflicts, and track knowledge history via a hosted SaaS platform. It enables efficient hierarchical information retrieval and semantic search while keeping token usage constant as memory scales.

Turns team chat (Slack, Discord, MS Teams, Mattermost) into a living knowledge base - a typed knowledge graph plus auto-generated wiki - exposed to AI agents through a 28-tool MCP server with semantic search, expert finding, and decision tracing.

Local, zero-dependency memory MCP for AI agents: agents stage, you promote, every entry has a receipt.

Seven-layer agent memory over MCP. zenbrain_store routes each memory itself: steps to a procedure, emotional weight to an episode, high confidence to a pinned core memory, the rest to a semantic fact. zenbrain_recall returns each result with its id and layer; zenbrain_forget deletes one memory by both; zenbrain_consolidate runs one sleep-like pass. Local SQLite file, no account, no network call.

Provides a local, adaptive memory layer that lets AI agents persist and retrieve decisions, constraints, and lessons across sessions without requiring an LLM API key.

Design contract layer for AI agents. Scans Figma, code, Storybook, and token files, reconciles conflicts, and serves a single machine-readable source of truth so every agent gets the same authoritative design rules before it builds. Local-first.