"General context or information search" 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.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

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

A persistent memory and context management system for AI CLI tools that utilizes a three-layer architecture and semantic search to prevent context loss between sessions. It provides time-aware orientation and smart memory routing to help AI agents maintain project knowledge and architectural decisions.

An MCP server that provides persistent, cross-session memory and team knowledge sharing for AI development workflows. It enables project DNA scanning, semantic search, context budgeting, and git-aware indexing to prevent AI context loss between sessions.

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 agents to import selected conversation archives, search and edit an Obsidian-compatible Markdown memory wiki, and read/write shared context across AI clients through MCP.

Enables LLM assistants to store, retrieve, and update user-specific context memory including travel preferences and general information through a chat interface. Provides analytics on tool usage patterns and token costs for continuous improvement.

Provides redacted access to a private local knowledgebase for coding agents, allowing them to inspect files while hiding sensitive names and identifiers.

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 users to search locally across imported Claude, ChatGPT, and Claude Code conversations and retrieve surrounding turns with citations, while keeping indexing and storage on their own computer.

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.

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.

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).

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.

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.

Durable project-memory MCP: decisions, constraints, and pipelines across Claude sessions

Persistent project context (AGENTS.md), cross-session memory, and identity — discoverable by any MCP client.

Provides persistent personal context storage across AI conversations, allowing AI assistants to remember user preferences, project conventions, and other personal information between sessions.

Enables AI assistants to search and retrieve encrypted local screen history, including OCR text, recent activity, context around moments, daily rollups, and screen snapshots.

Enables LLMs to access a user's personal writing context—voice, style, opinions, expertise, projects, and communication patterns—via curated markdown files, helping the LLM match the user's voice when generating written content.

Long AI conversations fail in predictable ways. Context-First fixes all four: Failure Mode What Goes Wrong Context-First Solution Context Drift AI forgets earlier decisions and intent as the conversation grows context_loop + detect_drift continuously re-anchor every turn Silent Contradiction New inputs silently overrule established facts — the AI doesn't notice detect_conflicts compares every inp

Intelligent context manager for AI coding assistants that uses a three-level memory system (core, active, archive) to remember project context across conversations.

A system for defining, composing, and managing task-specific contexts for AI-powered development, enabling consistent, high-quality results.

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

Stores personal context as markdown files and exposes them via MCP for AI assistants to retrieve user preferences, skills, and background.

Provides persistent memory and a codebase knowledge graph for AI coding assistants, enabling shared context across multiple tools like Claude, Cursor, and ChatGPT, with significant token reduction.