"Information about Unreal Engine" matching MCP servers:

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

MCP server for managing and serving dynamic prompt templates using elegant and powerful text template engine. Create reusable, logic-driven prompts with variables, partials, and conditionals that can be served to any compatible MCP client like Claude Code, Claude Desktop, Gemini CLI, etc.

Enables LLM agents to play six-on-six Pokémon battles with hidden information, a full move set, and a deterministic battle engine, including local play, reproducible matches, and benchmarking against AI opponents.

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.

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.

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

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.

I gave Claude a mass grave of 200 markdown files and now it remembers my entire project between sessions. No cloud, no API keys, one 10mb Go binary, and private. Stateless Agent Memory Engine is your synapse to the CLI.

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.

Persistent memory engine for AI coding agents. Single Go binary, zero runtime dependencies, MCP-native. Stores, searches, and deduplicates memories across sessions using embedded SQLite with hybrid FTS + semantic search, memory decay, relation graph, and token-budget context assembly.

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

Content-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON

Open source SQL-native query and provisioning engine for cloud and SaaS infrastructure. Ships as a single signed binary with MCP tools for provider discovery, schema exploration, queries, and lifecycle operations.

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

Enables AI coding assistants to search and retrieve information from a locally ingested knowledge base using hybrid search, grounded in user-curated documentation.

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.

Query WHOIS/RDAP information for domains, IP addresses, CIDR prefixes and ASNs. Results are normalized to RDAP-style (RFC 9083) JSON. Public instance of the open-source KincaidYang/whois server, which can also be self-hosted.

Scans self-hosted, multi-tenant AI-agent platforms for tenant-isolation gaps. Native Go MCP server exposing a scan tool that wraps the CLI's audit engine.

A high-performance, asynchronous MCP server that provides comprehensive Google search and web content scraping capabilities through the Firecrawl API, designed for LLMs to retrieve external information efficiently.

self-hosted development harness (Go engine + CLI + desktop, Linux first) · brief → requirements → spec → plan → build → review → release · verdicts are exit codes, never model opinions · local models first (llama.cpp, vLLM) beside any OpenAI-compatible or Anthropic endpoint · operable by humans or by other agents over MCP with recorded, attributed decisions