"General context or information search" matching MCP servers:

Enables coding agents to reduce context tokens by requesting file outlines, individual symbols, compact searches, and condensed test, lint, and type-check failures instead of reading whole files or raw command output.

Enables Claude Desktop and Claude Code to list, search, and read files, run one terminal command, query SQLite, and manage named scheduled tasks without calling the Claude API.

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

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

A server that manages conversation context for LLM interactions, storing recent prompts and providing relevant context for each user via REST API endpoints.

Enables semantic search of shell session history, matching user intents to relevant CLI commands for keyboard-first developer workflows.

A powerful MCP server that brings the skills.sh ecosystem directly to your AI agents, enabling effortless discovery, installation, and management of skills.

Enables semantic search of shell session history, matching natural-language intents to past CLI commands, including Warp sessions, for fast keyboard-first command discovery and reuse.

An MCP server that folds function and class implementations into structural skeletons while preserving signatures, interfaces, and docstrings, reducing tokens by up to 90%+ for AI agents. Exposes tools to get repository skeletons, unfold specific symbols on demand, and retrieve exact token and prompt cost savings metrics.

A lightweight service that enables AI assistants to execute AWS CLI commands through the Model Context Protocol (MCP), allowing AI tools to retrieve AWS documentation and interact with AWS services.

Connects AI assistants to a Kali Linux terminal for executing security tools, managing interactive shell sessions, and performing penetration testing, CTF challenges, and security research tasks.

Enables AI assistants to understand lab topologies, follow operational principles, and interact with real network devices through observable terminals while keeping private access information hidden.

An enhanced Model Context Protocol filesystem server that fixes common limitations and adds features like glob pattern search, head/tail reading, intelligent file editing with syntax validation, and new tools for delete and command execution.

Developers: Search your zsh, bash, or fish shell history from Claude Code, Cline, Cursor, Zed, or any MCP client using tools like search_history (full-text with timestamp/CWD/exit code), recent_in_dir, failed_commands, and command_chains for multi-step sequences. Reindex after new activity. Local-only SQLite FTS5 with secrets redacted before storage.

Enables MCP clients to safely read, write, edit, search, and optionally run commands within one or more named project workspaces, while maintaining a work journal and tracking changes.

A minimal MCP server that exposes a single tool to query local hardware information (OS, CPU, memory, architecture).

An MCP server that intelligently filters and compresses tool outputs to reduce context window usage, saving up to 90% of tokens by removing noise such as passing tests and redundant information.

A Model Context Protocol server that exposes a MeshCore node and the mesh reachable through it as a clean, high-signal interface for AI agents or tools.

Enables AI agents to read tmux pane content on-demand, providing context-aware assistance during interactive terminal sessions.

Enables LLM agents to run commands, manage interactive or background sessions, and transfer files on remote hosts under policy-based authorization, command classification, approval gates, and full audit logging. Supports per-host profiles with role-based rules, streaming file transfers that keep bytes out of model context, and a configurable approval policy for destructive or privileged work.

A Model Context Protocol (MCP) server that provides a bridge to Anthropic's Claude CLI. It allows MCP-compliant clients like Claude Desktop or Gemini to start new chat sessions or continue existing ones.

Lets AI coding agents read an outline of a large file or a digest of noisy command output instead of the full text, while every hidden line stays retrievable on disk through a run handle. Agents can request only the line ranges, slices, or grep matches they need and list recent runs, cutting token spend without losing information.

Enables MCP clients such as Claude and Cursor to control an isolated cloud machine with a real browser, shell, filesystem, and web access, so agents can navigate pages, click and type, run commands, manage files, and search or fetch web content.

Enables AI assistants to execute SSH commands and perform token-efficient file operations on remote servers via the Model Context Protocol.

Provides Ubuntu system information (CPU, memory, disk, network, services, processes) to Bob via natural language queries.

Enables MCP-based communication with existing interactive terminal channels, such as shells or agent CLIs, by listing, reading, writing text, and sending control signals to tmux sessions without creating or managing them.

A stateless MCP server that wraps the headless Gemini CLI, providing tools to send prompts to Google's Gemini models and receive text responses. It supports both simple prompts and prompts with contextual information.

A Model Context Protocol server that generates lightweight ASCII charts directly in terminal environments, supporting line charts, bar charts, scatter plots, histograms, and sparklines without GUI dependencies.

Enables users to interact with container registries through the ORAS CLI, providing information about container images, platforms, and signatures via natural language queries.

Enables AI coding assistants to capture large command outputs into an ephemeral in-memory buffer and search them via hybrid BM25 and semantic retrieval with line-level context.