No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by dynamiclu · MCP Server · ★ 51
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is CeCeData-Agent safe to install? View the security audit →
Mcp-ChatBI 介绍 基于 Mcp 架构的 ChatBI，是一种数据分析智能体的解决方案。 本项目主要解决ChatBI常见的三个问题 1、如何保障数据的100%的准确性？ 由于模型存在幻觉，无论是NL2SQL、NL2Code，都无法保障数据100%的准确。且数据的准确性又是BI系统的红线，因此，本项目使用NL2Tools的方案，Tools可以是Headless BI的服务，也可是API。 2、一次对话如何查询多个指标？ 过去一次对话只能查一个指标，若多个指标需要工程层面去拆解，架构的复杂度非常高，本项目，利用模型的任务规划、推理能力，模型自动拆解多个指标，并按照顺序调用Tools，并返回结果。 3、如何让数据分析的链路自动化？ 常见的数据分析方法，如对比分析、多维钻取、归因运算等，它常常伴有复杂的逻辑推理，当前大模型能力突飞猛进，已经具有复杂问题的推理能力，因此，本项目，利用模型推理能力，自动生成数据链路，并逐次调用Tools，返回结果，最后总结分析。 部署 环境配置 确保你的机器安装了 Python 3.10 - 3.12 大模型配置 启动接口 启动Gradio 演示 https://www.bilibili.com/video/BV1b95vzPEjf/
| Stars | 51 |
| Forks | 12 |
| Language | Python |
| Category | MCP Server |
| Quality Score | 59.2515737680474/100 |
| Open Issues | 3 |
| Last Updated | 2025-05-19 |
| Created | 2025-05-05 |
| Platforms | mcp, python |
| Est. Tokens | ~2k |
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CeCeData-Agent is 基于 Mcp 架构的 ChatBI，是一种数据分析智能体的解决方案. It is categorized as a MCP Server with 51 GitHub stars.
CeCeData-Agent is primarily written in Python. It covers topics such as aiagent, chat-bi, llm.
You can find installation instructions and usage details in the CeCeData-Agent GitHub repository at github.com/dynamiclu/CeCeData-Agent. The project has 51 stars and 12 forks, indicating an active community.
The top alternatives to CeCeData-Agent on Agent Skills Hub include Noema-Declarative-AI, Autono, zettelkasten-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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.
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