"General Information About Time" matching MCP servers:

A Linux system monitoring MCP server that provides real-time information on CPU, memory, disk, network, processes, Docker, security, and more via MCP tools.

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

Give your coding agent the dependency graph it is about to change: scan, diff, explain, audit.

The Time MCP Server is a Model Context Protocol (MCP) server that provides AI assistants and other MCP clients with standardized tools to perform time and date-related operations. This server acts as a bridge between AI tools and a robust time-handling back

CNN's Fear & Greed index for the US stock market: current score and rating, the seven component indicators, and about a year of daily history. Go standard library only, no API key required.

Road routing, travel-time matrices, geocoding, and fleet optimization for Southeast Asia.

Indexes the session histories Claude Code, Codex CLI and opencode already write into a local searchable memory. MCP recall tools, session-start auto-recall, secret redaction at index time, cross-machine sync over SSH.

Probe LLM API endpoints and report health metrics including time to first token, latency, and throughput.

Scans source code for possible Korean compliance risks (PIPA, Network Act, Credit Information Act, E-Commerce Act) and maps findings to specific legal provisions. Runs locally, exposes MCP tools for scanning and law lookup, and does not constitute legal advice. That's hedged (matches the project's "possible risk / not legal advice" rule), keyword-rich for search, and names all four laws.

CI health reports for GitHub Actions: flaky tests named from logs, wasted compute priced in dollars, zombie crons, PR feedback time, plus 21 lint rules with safe auto-fixes. Read-only MCP tools wrap the same Go binary.

A local AST and type-aware Go repository context indexer that uses CHA to map symbols, call graphs, dependencies, and architecture patterns. Built for AI coding agents that need to navigate and reason about large Go codebases without reading raw files.

An MCP server that purchases small factual datasets on demand via x402 micropayments (about $0.01 per call), offering tools for financial research, fuel prices, air quality, and border crossings while supporting preview-only mode and configurable spending limits.

Self-hosted Go MCP server that crawls documentation sites into local Markdown/JSONL corpora and lets agents read pages, run crawls, check freshness, and diff changes with zero network access at read time. Single static binary, no CGO, Apache-2.0.

About The fast, idiomatic way to build MCP servers in Go. Gin-like DX with struct-tag auto schema, middleware, adapters for Gin/OpenAPI/gRPC.

A git-native merge gate. mergelock check compares two branches before they meet and reports the conflicts git itself won't tell you about: a function whose signature changed on one branch while the other branch added calls to the old signature, a symbol deleted on one side but still used on the other, the same function written twice.

Enables AI agents to ask what changed in a time window and get back a short, ranked answer across metrics, logs and traces, with the service where a failure started identified and its echoes in other services labelled as such. Read-only and deterministic, it scores changes against a baseline and trims results to a token budget, so agents avoid paging through raw queries and blaming the loudest symptom.

MCP server that exposes any Pumperly instance to LLMs, enabling real-time fuel price queries, station search, route planning, and geocoding.

Query and interact with kubernetes environments monitored by Metoro. Look at APM, metrics, traces, profiling information with LLMs.

Official Thordata MCP server. Gives AI assistants real-time web scraping, SERP extraction, and global residential proxy capabilities to access live web data seamlessly.

Seamlessly bring real-time production context—logs, metrics, and traces—into your local environment to auto-fix code faster.