Baidu Maps▌
by baidu-maps
Integrate Baidu Maps API for geocoding, route planning, and location search within the Baidu Maps ecosystem.
Integrates with Baidu Maps API for location-based operations including geocoding, route planning, and location search within the Baidu Maps ecosystem.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Apps serving Chinese users and locations
- / Location-aware AI agents and chatbots
- / Travel and logistics applications in China
- / Geographic data analysis projects
capabilities
- / Convert addresses to coordinates (geocoding)
- / Find addresses from coordinates (reverse geocoding)
- / Search for nearby businesses and POIs
- / Plan driving, walking, cycling, and transit routes
- / Get weather and traffic information
- / Locate IP addresses geographically
what it does
Connects to Baidu Maps API to provide location services like finding addresses, planning routes, and searching for points of interest in China.
about
Baidu Maps is an official MCP server published by baidu-maps that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Baidu Maps API for geocoding, route planning, and location search within the Baidu Maps ecosystem. It is categorized under developer tools.
how to install
You can install Baidu Maps in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Baidu Maps is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
[](https://github.com/baidu-maps/mcp)  [](LICENSE) [](https://pypi.org/project/mcp-server-baidu-maps/) [](https://www.npmjs.com/package/@baidumap/mcp-server-baidu-map)
## 🚀 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)](https://modelcontextprotocol.io/introduction), 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 platform. - **Rich LBS Capabilities:** Geocoding, reverse geocoding, POI search, route planning (driving, walking, cycling, transit), weather, IP location, real-time traffic, and more. - **Cross-Platform SDKs:** Official Python and TypeScript SDKs, easy CLI and cloud deployment. - **Enterprise-Grade Data:** Powered by Baidu Maps' authoritative, up-to-date geospatial data. - **High Performance & Stability:** Recommended SSE (Server-Sent Events) access for low latency and high reliability. - **Open Source & Extensible:** MIT licensed, easy to customize and extend. Whether you are building a travel assistant, logistics platform, smart city solution, or an LLM-powered agent, Baidu Map MCP Server provides the essential geospatial intelligence and tools you need. The MCP Server architecture enables: - **Seamless AI Integration**: Allows LLMs and agents to understand and process location data naturally - **Contextual Understanding**: Provides rich geospatial context for more intelligent decision-making - **Standardized Interfaces**: Consistent API design following MCP principles for easy integration - **Scalable Implementation**: Suitable for projects of any size, from small applications to enterprise solutions Whether you're building a navigation app, delivery service, smart city solution, or enhancing an AI agent with location awareness, Baidu Map MCP Server provides the tools and infrastructure you need to succeed. ## 🛠️ Supported Tools & APIs Baidu Map MCP Server provides the following MCP-compliant APIs (tools): | Tool Name | Description | |--------------------------|----------------------------------------------------------------------------------------------| | `map_geocode` | Convert address to geographic coordinates. | | `map_reverse_geocode` | Get address, region, and POI info from coordinates. | | `map_search_places` | Search for global POIs by keyword, type, region, or within a radius. | | `map_place_details` | Get detailed info for a POI by its unique ID. | | `map_directions_matrix` | Batch route planning for multiple origins/destinations (driving, walking, cycling). | | `map_directions` | Plan routes between two points (driving, walking, cycling, transit). | | `map_weather` | Query real-time and forecast weather by region or coordinates. | | `map_ip_location` | Locate city and coordinates by IP address. | | `map_road_traffic` | Query real-time traffic conditions for roads or regions. | | `map_poi_extract`* | Extract POI info from free text (requires advanced permission). | > *Some advanced features require additional permissions. See [Authorization](#authorization) for details. All APIs follow the MCP protocol and can be called from any MCP-compliant client, LLM, or agent platform. ## ⚡ Quick Start ### 1. Get Your API Key Register and create a server-side API Key (AK) at [Baidu Maps Open Platform](https://lbsyun.baidu.com/apiconsole/key). **Be sure to enable “MCP (SSE)” service for best performance.** ### 2. Python Integration Install the SDK: ```bash pip install mcp-server-baidu-maps ``` **Run as a script:** ```bash python -m mcp_server_baidu_maps ``` **Configure in your MCP client (e.g., Claude, Cursor):** ```json { "mcpServers": { "baidu-maps": { "command": "python", "args": ["-m", "mcp_server_baidu_maps"], "env": { "BAIDU_MAPS_API_KEY": "
FAQ
- What is the Baidu Maps MCP server?
- Baidu Maps is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Baidu Maps?
- This profile displays 43 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★43 reviews- ★★★★★Ganesh Mohane· Dec 24, 2024
I recommend Baidu Maps for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Emma Singh· Dec 20, 2024
Baidu Maps reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Charlotte Huang· Dec 16, 2024
We evaluated Baidu Maps against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Ira Reddy· Dec 12, 2024
Useful MCP listing: Baidu Maps is the kind of server we cite when onboarding engineers to host + tool permissions.
- ★★★★★Sakshi Patil· Nov 15, 2024
According to our notes, Baidu Maps benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Isabella Khanna· Nov 11, 2024
Baidu Maps has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Nikhil Lopez· Nov 7, 2024
Baidu Maps is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Nikhil Haddad· Oct 26, 2024
According to our notes, Baidu Maps benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Chaitanya Patil· Oct 6, 2024
Baidu Maps is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★James Diallo· Oct 2, 2024
Baidu Maps is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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