by NPC-Worldwide · MCP Server · ★ 1.5k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
pip install npcpy
Taken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 1,523 |
| Forks | 139 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 66/100 |
| Open Issues | 7 |
| Last Updated | 2026-11-02 |
| Created | 2024-09-27 |
| Platforms | cli, mcp, python |
| Est. Tokens | ~22k |
npcpy is a library that provides key primitives for research and development with multimodal language models, agentic AI, and knowledge graphs. Its flexible framework makes it easy to engineer powerful AI applications with support for local (, , , ) and cloud providers.
Looking for a npcpy alternative? If you're comparing npcpy with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
[DEPRECATED] Legacy Klavis AI MCP integrations. See klavis.ai for RL environments and training data.
Token conscious database MCP server for Postgres, MySQL, SQL Server, Oracle, MariaDB, SQLite.
Desktop AI Assistant powered by GPT-6, GPT-5, Gemini, Claude, Grok, Ollama, DeepSeek, Perplexity, and more - c
A set of tools that gives agents powerful capabilities.
Agent samples built using the Strands Agents SDK.
An example agent demonstrating streaming, tool use, and interactivity from your terminal. This agent builder c
Explore other popular mcp server tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: