"A repository for hosting and sharing Java libraries" matching MCP servers:

Query Langfuse traces, schema and datasets, scores and metrics, debug exceptions, analyze sessions, and manage prompts. Full observability toolkit for LLM applications.

Enables interaction with Steam: search games, get store details, reviews, prices, discounts, news, and player profiles, libraries, and achievements via the Steam Web API.

Provides AI coding assistants with access to AccelByte Gaming Services (AGS) Extend SDK functions and models through searchable symbols and detailed documentation. Helps generate SDK code by exposing context about available functions, models, and templates for creating Extend app projects across multiple languages (Go, C#, Java, Python).

MCP server that injects verified FTC documentation and code examples into AI assistants, enabling teams to write correct, competition-ready Java robot code through natural language.

Educational MCP server with 17 tools for mathematical calculations, matrix operations, statistical analysis, and data visualization. Includes persistent workspace, cloud hosting, and safety features like restricted eval.

Enables gene set enrichment analysis using the Enrichr API across hundreds of gene set libraries including Gene Ontology, pathways, diseases, tissues, drugs, and transcription factors. Returns only statistically significant results for interpretation.

A production-grade MCP server for the Code-Fundi API, enabling AI agents to map codebases, search semantically, and analyze blast radius. It provides tools for repository management, AI-powered research, and impact analysis before shipping changes.

Enables AI assistants to search, list, and retrieve documentation from the Slice.js official GitHub repository. It provides full-text search capabilities and can deliver individual doc pages or a complete documentation bundle for comprehensive LLM context.

Local-first engineering memory and code-graph tools for coding agents. Supports private session handoffs, code-cited knowledge, repository graph inspection, and optional Git-backed team sharing.

Enables AI assistants to control Spotify playback, search the full catalog including podcasts and audiobooks, manage libraries and playlists, and understand listening taste.

Enables AI assistants to search and retrieve records from the Digital Guitar Archive database using full-text search and field-specific filters. It provides tools for fetching detailed record information and listing historical source libraries.

A lightweight MCP server that enables AI assistants to execute local development tools and retrieve system status with low latency over stdio or HTTP.

An MCP server that provides AI clients with access to developer documentation via llms.txt files. Exposes tools, resources, and prompts.

A local-first, no-API-key MCP server that enables LLMs to search the web, fetch pages, and read documents using multiple engines and smart fallbacks.

Enables AI assistants to perform Google searches with real, resolvable sources and transparent search terms.

MCP server for overseer.nvim — gives any MCP-capable coding agent running inside Neovim (Claude Code, Codex, Gemini CLI, opencode) the same visibility and control over tasks the user has: list, run, tail, restart, stop. Agent-started dev servers land in the user's own task list instead of becoming invisible orphaned processes.

Enables AI models to access and search offline Wikipedia and other knowledge bases stored in ZIM format files. Provides intelligent content retrieval, structured browsing, advanced search capabilities, and metadata extraction for comprehensive offline knowledge access.

Read-only MCP server for Grafana Loki: lets AI agents (Claude Code, Cursor, Copilot) discover labels, read bounded LogQL pages, count lines by label or time, and export logs — across several Loki stands

Enables multi-level reasoning with configurable depth levels, session-based state management, structured thought input, and real-time trace resources.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.