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 baidu-maps · MCP Server · ★ 436
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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中文| English 🚀 Introduction Baidu Map MCP Server is a fully MCP-compliant, open-source Location-Based Service (LBS) solution, providing a comprehensive suite of geospatial APIs and tools for developers and AI agents. As the first map service provider in China to support the Model Context Protocol (MCP), Baidu Map MCP Server bridges the gap between large language models (LLMs), AI agents, and real-world location data and services. With Baidu Map MCP Server, you can easily empower your applications, LLMs, and agents with advanced mapping, geocoding, POI search, route planning, weather, traffic, and more — all via standardized, developer-friendly MCP interfaces. Key Features: Full MCP Protocol Support: Seamless integration with any MCP-compliant agent, LLM, or p
| Stars | 436 |
| Forks | 47 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 62.8131767803803/100 |
| Open Issues | 14 |
| Last Updated | 2025-08-22 |
| Created | 2025-03-20 |
| Platforms | mcp, python |
| Est. Tokens | ~387k |
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mcp is Baidu Map MCP Server. It is categorized as a MCP Server with 436 GitHub stars.
mcp is primarily written in Python. It covers topics such as lbs, map, maps.
You can find installation instructions and usage details in the mcp GitHub repository at github.com/baidu-maps/mcp. The project has 436 stars and 47 forks, indicating an active community.
mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp on Agent Skills Hub include CoexistAI. 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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