"A server providing context 7" matching MCP servers:

A self-hosted MCP server that enables AI coding agents to read, edit, search, and run code in local projects with human review loops and policy controls.

Enables users to index local Markdown articles and then search, filter, list, and retrieve them through MCP tools while keeping all content on the local machine.

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.

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.

A Model Context Protocol server providing tools for DB queries, API calls, file I/O, and text transformations, enabling AI agents like Claude to perform real-world actions.

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.

A local-first MCP server that gives AI coding assistants persistent, structured, human-readable memory for a software project by storing project knowledge as Markdown files in the project's .dev-context-memory/ folder.

Enables AI assistants to read, edit, and execute Jupyter notebook files (.ipynb) with live kernel output, without requiring JupyterLab.

Enables natural-language diagnosis of Linux system issues by providing sandboxed, typed tools for system, service, filesystem, and git inspection, with human approval for mutating actions.

A read-only MCP server that exposes AI Disk Doctor Core's scan, model inventory, and history tools via local stdio, with no mutation or shell capabilities.

A sophisticated MCP server providing powerful file search capabilities including single file search, recursive directory search, and file information retrieval.

Provides direct, unsandboxed local machine access via filesystem, Python, Node.js, and shell commands for MCP agents.

locidx

Enterprise-grade PDF engine for AI agents. Pure TypeScript, zero-dependency, sandboxed, and local-first. Exposes 28 tools to generate ISO-compliant PDF/A documents, inspect layouts, extract text, handle digital signatures (PKCS#7), and render high-performance tables (10,000+ rows/s).

Enables AI coding agents to watch directories for live filesystem changes (create, modify, delete, move) via the Model Context Protocol, providing event-driven access without busy-polling.

A comprehensive and efficient Model Context Protocol server for task management that works with Claude, Cursor, and other MCP clients, providing powerful search, filtering, and organization capabilities across multiple file formats.

Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.

Bridges a remote, cloud-hosted agent to your local machine, exposing Pi's coding tools (read, write, edit, ls, grep, find, bash, powershell) rooted at a directory you choose. It also adds a human channel so the agent can message, question, and wait on you through tool calls instead of a platform chat UI, with OAuth 2.1 or bearer-token authorization and no persistence between runs.

Enables AI agents to access a local-first personal context and memory layer through MCP tools for bootstrapping user context, semantically searching Markdown vault knowledge, reading files, listing topics, and adding controlled memories. It provides deterministic context retrieval, provenance-cited vector search, and secure vault operations while keeping data local and model-independent.

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.

A Model Context Protocol (MCP) server that enables AI assistants to perform comprehensive file operations including finding, reading, writing, editing, searching, moving, and copying files with security validations.

A comprehensive MCP server that enables AI models to perform local file operations, command execution, and task management across multiple platforms. It features advanced capabilities like row-level file editing, directory searching, and system monitoring with built-in security filters.

Zero-Knowledge Cloud Drive for Humans and Agents. Client-side AES-256-GCM encryption with 7 MCP tools for secure file vault management — the server never sees plaintext data.

An Amazon S3 Model Context Protocol server that allows Large Language Models like Claude to interact with AWS S3 storage, providing tools for listing buckets, listing objects, and retrieving object contents.

A cross-platform MCP server providing persistent storage for AI assistants to store, retrieve, and manage memories across conversations. It features keyword and tag-based search capabilities using a local JSON file for data persistence.

An MCP server for 7-Zip archive operations (list, extract, create) with dual-engine architecture and sandbox security features. Note: currently a learning project and not yet functional.

A Model Context Protocol server that lets Windows-side AI agents operate on WSL distributions like a local environment, providing file read/write/edit, command execution, process management, log streaming, and content search via UNC paths and persistent shells.

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.

Enables agents to compress JPEG, PNG, and WebP images to a target byte size, split PDFs by page or ranges, and remove image metadata while processing local files without sending file bytes over the network.