Save permanent operational rules learned during coding sessions. These rules persist and auto-surface in search results to prevent AI agents from repeating mistakes.
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510,487 tools. Updated 2026-09-04 03:24
"Resources to Improve AI Coding Ability in C++ and Rust" matching MCP tools:
- Generate or improve AI social media captions. Provide a draft and target platforms for tone-aware text. Costs 1 AI credit.MIT
- Scan source code with Semgrep to find security vulnerabilities, bugs, and anti-patterns. Supports multiple languages including Python, JavaScript, Java, Go, C, C++, Ruby, and more.MIT No Attribution
- Generate or improve social media captions with AI. Optionally refine a draft and tailor tone for specific platforms.MIT
- Delegate a scoped coding task to an external AI worker in an isolated git worktree with independent verification.MIT
- Compile C++ changes in Unreal Engine without closing the editor, using Live Coding for rapid iteration on function body modifications.MIT
Matching MCP Servers
- AlicenseNot gradedqualityCmaintenanceMCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.MIT
- AlicenseNot gradedqualityBmaintenanceCaptures user corrections and improvements during interactions, logs them, finds recurring patterns, and prescribes preventive fixes like CLAUDE.md rules, skills, scripts, or MCP tools to avoid repeating mistakes.12MIT
Matching MCP Connectors
Persistent project context — Rust-native MCP server. IANA-registered .faf format.
Read-only MCP over the LivingMeta AI-in-Research corpus: 12,400 papers, gaps, priority agenda.
- Analyzes your AI collaboration patterns to identify blind spots in project setup, development habits, and knowledge gaps, helping you improve how you work with AI coding tools.MIT
- Resolve a symbol to its exact or fuzzy definition(s) across C, C++, Python, Rust, TypeScript, Kotlin, and Swift. Get candidates with signatures, locations, and variants to disambiguate before graph or read calls.MIT
- Submit a coding task to have AI write code, commit to GitHub, and optionally deploy. Returns a task ID to poll for results.MIT
- Aggregate and rank cumulative AI spending across all projects and sessions from detected coding tools.MIT
- Analyze cache savings from prompt caching across all detected AI coding tools.MIT
- Score your prompt against a validated rubric covering Specificity, Clarity, Scope, Verification, and Constraint Calibration to improve your AI coding assistant interactions.MIT
- Analyze AI-assisted coding adoption over time, compare human vs AI contributions by language, and track efficiency trends to understand AI's impact on development.ISC
- Search AiWiki for AI-coding pitfalls and LLM privacy protection answers. Get cited explanations and solutions for recurring AI failures.CC BY-SA 4.0
- Train AI agents using human-in-the-loop feedback to optimize prompts and improve multi-agent system performance.MIT
- Detects critical secure coding issues in C/C++ source code, including stack buffer overflows, unsafe function usage, format string exploits, and memory leaks.MIT
- Generate configuration files for AI coding assistants, embedding React Native best practices, API validation rules, and project context to guide AI code suggestions.MIT
- Retrieve Andrej Karpathy's core AI coding rules to prevent over-engineering, bad assumptions, and collateral edits. Filter by category like simplicity-first or surgical changes.MIT
- Store technical decisions, project rules, or conventions in persistent context so important information remains available across AI coding sessions.MIT
- Packs compact project context from git diff into a file, enabling AI agents to understand the project structure and start or switch coding tasks with low token cost.MIT