"A server for finding coding resources and programming information" matching MCP servers:

Provides coding tutoring by running checks for common programming tasks (palindrome, prime, Fibonacci, GCD, etc.) and explaining the results in plain language.

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

Provides 23 modular tools for competitive programming, including problem analysis, algorithm planning, code generation, verification, testing, code review, and learning assistance.

MCP server for coding-os, a cognitive operating system that gives AI agents memory, structure, and discipline. It exposes tools for project management, task tracking, knowledge graphs, and cognition traces to Claude Code and Codex.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Enables AI agents to programmatically create and manipulate Qt-based visual programming flows, including device model parsing and port connection, through natural language.

Provides safe repository operations, skill reading, command execution, and test detection for coding agents over stdio.

Enables supervising and orchestrating local coding agents like Muse and AGY through a client-agnostic MCP interface, with isolated Git worktrees, safe execution, and deterministic verification.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables delegating coding tasks to multiple AI CLI agents (Claude Code, Antigravity, Codex, Cursor) with session memory and diff inspection.

Enables external AI clients to invoke an autonomous coding agent that plans, fixes, and self-corrects code in an isolated Docker sandbox via run_task and run_tests tools.

Provides AI coding assistants with code search, impact analysis, task chaining, and persistent memory to turn AI demos into deliverable code.

Enables AI coding assistants to search a local codebase semantically using AI embeddings, finding relevant code by meaning instead of exact keywords. It also provides package version lookup, manual reindexing, cache clearing, workspace switching, and server health status tools.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI assistants to search a codebase by meaning using local AI embeddings, finding relevant code even when terminology differs from the query. Also provides package version lookup across 20+ registries, workspace switching, reindexing, cache clearing, and status checks.

Enables AI coding assistants to search a codebase by meaning using locally generated embeddings, so relevant code is found even when terminology differs from the query, along with package version lookup, reindexing, cache reset, workspace switching, and status tools. Indexing runs progressively in the background with CPU throttling and a SQLite cache, letting searches work immediately on large projects.

Enables AI agents to control a Playwright-driven browser through natural-language instructions, including page interaction and live browser task execution.

Enables AI agents to develop within a local project workspace by reading and modifying files, running commands and tests, checking Git state, and persisting progress as history sessions that can be restored in later conversations.

Provides a local long-term memory layer for AI coding tools like Cursor and Claude Code, enabling cross-session, cross-tool sharing of project facts, user preferences, decisions, and workflows.

Enables AI coding assistants to search codebases by meaning using local AI embeddings, with tools for indexing, cache and workspace management, status checks, and latest package-version lookup.