"Building a solid memory base for AI development" matching MCP servers:

A Model Context Protocol server that enables seamless integration between Claude AI and development tools like VSCode, Augment, Vercel, Airtable, and Square.

Persistent memory for any AI assistant. Zero token cost until recall. Stores memories in local SQLite, ranks by 6-factor scoring, returns results 79% smaller than JSON. Works with Claude, ChatGPT, Grok, Cursor, Windsurf, and any MCP client.

Provides real-time access to Japanese building standards laws and regulations from e-Gov API, helping prevent AI hallucinations by validating citations against original legal texts.

Provides persistent session memory for AI assistants, enabling them to store, search, and retrieve conversation summaries across sessions via the Model Context Protocol.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

Enables AI assistants to discover, retrieve, and compare official international development indicators from sources such as the World Bank, FAOSTAT, WHO, UNICEF, and IMF, while preserving source identifiers, units, and citations.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

Gives AI agents a shared, encrypted memory store accessible over MCP, with per-agent keys, per-namespace permissions, history and revert, attachments, and semantic search that handles Czech as well as English. Memories, agents, network access, and HTTPS can be managed through a self-hosted admin web on the same port, with backups being simply copies of the encrypted data file.

Enables extraction of live municipal building permits and code violations, owner skip tracing to phones and emails, and contractor/OFAC screening through MCP tools.

Enables AI agents to store, retrieve, and manage contextual knowledge across sessions using semantic search with PostgreSQL and vector embeddings. Supports memory relationships, clustering, multi-agent isolation, and intelligent caching for persistent conversational context.

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.

Provides comprehensive building and office address information queries for enterprises in China, including fuzzy enterprise search, office address details and statistics, and building information queries to support geographical distribution and occupancy analysis.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

Provides AI agents and teams with self-hosted adaptive memory, combining hybrid search and a self-improving neural memory graph to enable precise recall and serendipitous discovery across MCP-compatible clients.

Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.

Provides Claude Co-work with MCP tools to search and record organizational memory, including search, read, query, ingest, remember, sync, and status operations.

A TypeScript-based template for rapidly developing MCP servers with modular tool architecture, built-in validation using Zod schemas, and comprehensive error handling.

Enables AI agents to share a persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources like Obsidian vaults, code, and transcripts through MCP and REST APIs. Provides token-optimized, tiered retrieval with full-text and semantic search so every agent session builds on accumulated context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent shared knowledge from sources such as Obsidian, code, transcripts, bookmarks, and sessions. It provides token-efficient search, context briefings, and tiered memory through MCP and REST APIs.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Gives AI agents persistent, shared memory by ingesting notes, code, transcripts, and session captures, then serving token-optimized context and hybrid keyword/semantic search over MCP and REST. Agents can classify, promote, and synthesize stored material so each session builds on prior work instead of starting from zero.

Enables AI agents and clients to capture, classify, synthesize, and retrieve persistent knowledge from multiple sources through MCP and REST APIs, with tiered memory and token-optimized context.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

Provides a production-ready Model Context Protocol server with dual STDIO and Streamable HTTP transports, enabling file operations, memory, database queries, RAG, web search, GitHub integration, background tasks, and prompt-based workflows.

Enables AI agents to share persistent memory by ingesting, classifying, synthesizing, and retrieving knowledge through MCP and REST APIs, with token-optimized, tier-aware search and Obsidian sync.

Enables AI agents to share a persistent, self-improving memory by ingesting sources like Obsidian vaults, code, transcripts, and session captures, then serving them back through search, classification, synthesis, and token-optimized context briefings. It lets multiple agents read and write the same knowledge base, so each session builds on the last without copy-pasting context.

Enables AI agents to share persistent memory by ingesting sources, classifying and synthesizing knowledge, and retrieving token-optimized context through MCP and REST APIs.

Gives AI agents a shared persistent memory by ingesting notes, code, transcripts, bookmarks, and session captures into a SQLite FTS5 knowledge base they can search, read, classify, promote, and synthesize over MCP. This lets multiple agents retrieve tiered, token-optimized context and compound what they learn across sessions instead of starting from zero.

Enables AI agents to share persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from Obsidian vaults, code, documents, transcripts, bookmarks, and sessions through MCP and REST interfaces with token-optimized search.

Enables AI agents to share persistent, searchable memory across sessions by ingesting and organizing knowledge from sources like Obsidian, code, transcripts, and terminal sessions. It provides token-optimized retrieval, semantic and keyword search, and MCP/REST access so agents can capture, classify, synthesize, and reuse context.

Enables AI agents to capture, classify, synthesize, and retrieve shared persistent knowledge across sessions via MCP, with tiered memory, full-text and semantic search, and token-efficient context briefings.

A persistent memory system that captures, classifies, synthesizes, and retrieves knowledge for AI agents, turning raw information into refined, retrievable knowledge that compounds intelligence over time.

Serves AI agents a shared persistent memory that ingests Obsidian vaults, code, transcripts, bookmarks, and session or bug-fix captures, then classifies, promotes, and synthesizes them into tiered, retrievable knowledge. Exposes token-optimized context briefings and hybrid keyword/semantic search over MCP so multiple agents can read and write the same brain and improve across sessions.

Enables AI agents to share persistent memory by ingesting knowledge from sources like Obsidian, code, documents, web clips, and terminal sessions, then providing token-optimized search, context, classification, synthesis, and capture tools through MCP and REST APIs.

Enables AI agents to share a persistent, self-improving memory by ingesting sources such as Obsidian vaults, code, transcripts, and bookmarks, then classifying, promoting, synthesizing, and retrieving that knowledge through token-optimized search across MCP and REST interfaces. Agents can capture sessions and fixes so context compounds across sessions and across Claude, Codex, Gemini, and any other MCP client.

Enables AI agents to share persistent, searchable memory by capturing, classifying, synthesizing, and retrieving knowledge from sources like Obsidian, code, transcripts, bookmarks, and sessions via MCP and REST with token-optimized context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent knowledge through MCP and REST APIs, so multiple agents can share memory and improve across sessions. It provides full-text, semantic, and hybrid search with token-optimized context retrieval from sources like Obsidian, code, transcripts, and terminal sessions.

Enables AI agents to share a persistent memory that captures, classifies, synthesizes, and retrieves information through token-optimized tiered search combining full-text and semantic queries. Multiple agents read from and write to the same store, so each session compounds intelligence over time.