"A server for finding information about context" matching MCP servers:

A lightweight MCP server for semantic search over markdown knowledge bases, enabling AI coding agents to index, search, and answer questions from local markdown documents.

An MCP server that reads all your Claude Code project memory files and exposes them as tools. Lets any Claude instance — in any project, or via Claude.ai — query your full project history and preferences.

Local-first memory for AI agents about the people in your life. MCP server + CLI on SQLite. Never phones home.

An MCP server for saving, managing, and enhancing context with pre-processing strategies like clarification, analysis, and search optimization.

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.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

Enables AI clients to read and write to a user-owned markdown bucket via S3-compatible storage, providing a persistent shared context layer across different AI tools.

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.

Enables persistent multimodal context storage for LLM agents with thread-based scoping, metadata filtering, and hybrid search capabilities.

MCP server for Context Studio, a file-based CMS for governed marketing documents and searchable collections, enabling retrieval and management of AI context via tools like scopes, document reads, and collection search.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

Multi-modal RAG engine for AI assistants. Stores conversation history, conclusions, diffs, error traces, and other development artifacts in LanceDB with vector search, multi-factor scoring, and an LLM-driven consolidation pipeline.

Enables AI coding agents to persist structured long-term memory (gotchas, architecture, API notes) in a .context folder and sync across devices and agents via Git.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

Provides an external context buffer so a local model can maintain session memory across long tasks and search and page through large documents without loading them whole into its limited context window.

A self-contained, in-memory graph database for AI Agents. Provides semantic code understanding through the Model Context Protocol (MCP).

A static, read-only MCP server that exposes a database knowledge base (schema, relationships, workflows, and reasoning patterns) so Claude Code / Copilot can reason about a database without a live connection.

A reusable MCP server providing shared, versioned context across AI agents and devices via a private GitHub workspace, with tools to discover projects, bootstrap, query, and close out task state.

A local semantic memory and code-indexer that uses AST parsing for structural understanding and persists architectural decisions to help AI assistants bypass context window limits.

MCP server for shared context caching with trust verification -- AI agents share and verify computed results to reduce token cost and increase reliability.