"Gathering Context from an Ubuntu Server" matching MCP servers:

An MCP server that exposes any REST API to LLMs through runtime discovery, providing tools to browse endpoints, fetch schemas, and apply business rules without hardcoding or schema duplication.

Enables users to expose arbitrary HTTP APIs as configurable MCP tools by defining tool specifications in a JSON configuration file.

Enables AI clients to perform sandboxed filesystem, directory, Ubuntu system, network, git, and code/text operations through 38 typed tools confined to a workspace. Ships with a browser-based web console that provides an interactive Linux terminal, a live tool playground, and real-time CPU, memory, and disk monitoring.

A secure protocol server that allows AI assistants to safely interact with Ubuntu systems through controlled file operations, command execution, package management, and system information retrieval.

An MCP server that records agent execution metrics and exposes a Context Window Explorer to visualize exactly what entered the model's context window across sessions, tokens, and tool calls.

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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.

Give your AI agents access to 8,500+ community curated awesome lists with over 1 million curated resources.

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

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

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 high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.

Provides persistent memory, identity, and context for Claude sessions through a memory graph, enabling recall, storage, and relationship management of entities and decisions.

Enables local-first context retrieval and management from JSONL and Markdown sources through MCP, with a rebuildable full-text search index and support for stable reference pagination.

MCP proxy that reduces context usage through semantic tool routing, enabling on-demand discovery and routing of relevant tools.

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.

Allows coding agents to securely retrieve curated, sealed Slack message bundles (capsules) via MCP, with full audit and expiration controls.

Enables querying Dutch property context for an address, returning building, energy, neighborhood, environment, heritage, and school data from public registers, with explicit match verification and signals.

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