"Resources or assistance for coding help" matching MCP servers:

MCP server that provides intelligent student assistance tools for academic calculations, attendance tracking, marks analysis, and syllabus reading via Groq LLM.

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.

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.

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

Provides AI coding assistants with local access to 4500+ MQL5 documentation files and supplementary ebooks via MCP, enabling efficient search, browsing, and error diagnosis for MQL5 development.

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

Local multi-version RAG MCP server for Keysight ADS help documentation, enabling search, AEL function lookup, and version comparison via natural language.

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.

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.

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.

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.

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 find relevant code by meaning through local AI embeddings and an indexed codebase, so searches work even when terminology differs. It also supports package-version lookup, reindexing, cache management, workspace switching, and server status checks.

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.

Enables AI coding assistants to search codebases by meaning using local AI embeddings, and provides tools for package version lookup, reindexing, cache clearing, workspace switching, and status checks.

A local MCP server that gives AI coding assistants read, write, search, and execute capabilities within a specified project directory. This Windows GUI build simplifies setup by eliminating manual Python, Node.js, or Docker installation.

Enables AI coding assistants to search codebases by meaning using local AI embeddings, with tools for indexing, cache management, workspace switching, status checks, and package version lookup. It supports incremental and progressive indexing with configurable embedding dimensions to find relevant code even when terminology differs.

Experimental MCP server for local LLM orchestration with filesystem tools (read, write, list, delete files) and a CLI agent that communicates via Ollama.