"Information or context about 'context7'" matching MCP servers:

Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.

A unified context layer that connects your local data — repositories, documents, remote machines, and notes — to LLM interfaces through the Model Context Protocol (MCP).

Provides AI agents with secure, read-only file system access to analyze and understand project codebases, enabling multi-repository context aggregation and cross-project code tracing.

A thin, local, read-only MCP gateway that exposes governed context documents and producer manifests as logical resources, enabling AI clients to access them without arbitrary filesystem access.

A Model Context Protocol (MCP) server that provides VSCode context and filesystem operations for AI assistants.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.

MCP server that gives AI chat real vision by reading images, files, logs, and zip archives via local OCR in any language, with privacy-first processing.

MCP server that helps AI coding agents understand a repository by providing lightweight tree/map, code search, and token-budgeted context packing tools without dumping the entire monorepo into the prompt.

MCP server providing lossless file reads with deduplication, per-repo token metering, and HMAC-signed context receipts. Enables auditable, vendor-neutral measurement of what an AI agent saw.

Provides tools to monitor host system health including CPU load, disk usage, and network status while enabling file system management tasks like searching and moving files. It includes built-in safety guards to prevent operations on critical system directories.

Compress OCR-heavy PDFs into dense packed images so agents can work with long visual documents.

Enables managing personal information with dynamic topic-based organization (tasks, meetings, contacts, etc.), supporting optional OTP authentication and AES-256 encryption for sensitive data with automatic backups.

A simple MCP server for local documentation with RAG capabilities, enabling AI assistants to access and search local documents.

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.

MCP server providing filesystem allowlist and pack-aware Qdrant RAG search. Enables reading files, listing directories, and performing filtered RAG searches with case and pack IDs.

Efficiently loads, caches, and delivers project file context for AI agents using the Model Context Protocol, reducing multiple file reads to a single call with LRU caching and security validation.

Provides owner-bound secure storage and retrieval of tool outputs via MCP tools, enabling trusted persistence for agents like Claude Code, Codex CLI, and OpenCode.

Enables AI agents to interact with both frontend and backend projects by providing file system access and context through the MCP protocol, facilitating full-stack development assistance.

A highly configurable MCP server with modular architecture supporting portfolio management and test tools over HTTP or stdio transport.

Enables AI assistants to read, search, and analyze local file systems with tools for reading file contents, listing directories, searching by patterns, and analyzing folder structures for context-aware queries.