Inmovilla MCP Server
Enables interaction with the Inmovilla real estate CRM platform to manage properties, clients, owners, and retrieve platform enumerations through natural language.
README
mcp-inmovilla
A Model Context Protocol (MCP) server that provides tools to interact with the Inmovilla API. This server enables LLMs to manage properties, clients, owners, and retrieve various enumerations from the Inmovilla real estate platform.
About MCP
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Learn more:
Installation
1. Clone and Install Dependencies
git clone <repository-url>
cd mcp-inmovilla
npm install
2. Build the Project
npm run build
3. Configure Environment Variables
Create a .env file in the project root with the following variables:
INMOVILLA_API_TOKEN=your_inmovilla_token_here
OPENAI_API_KEY=your_openai_api_key_here
Obtaining the Inmovilla API Token
To get your Inmovilla API token:
- Log in to Inmovilla CRM
- Navigate to Ajustes > Opciones > Token para API Rest
- Generate your token
- Copy the token to your
.envfile
Note: Tokens will automatically expire after 3 months of inactivity.
Obtaining the OpenAI API Key
- Sign up or log in to OpenAI Platform
- Navigate to API keys section
- Create a new API key
- Copy the key to your
.envfile
4. Start the Server
npm start
The MCP server will start on port 1337 at http://localhost:1337/mcp.
Project Structure
mcp-inmovilla/
├── src/
│ ├── tools/ # MCP Tools for Inmovilla API
│ │ ├── CreateClientTool.ts
│ │ ├── CreateOwnerTool.ts
│ │ ├── CreatePropertyTool.ts
│ │ ├── DeleteClientTool.ts
│ │ ├── DeleteOwnerTool.ts
│ │ ├── GetClientTool.ts
│ │ ├── GetEnumTool.ts
│ │ ├── GetOwnerTool.ts
│ │ ├── GetPropertyExtraInfoTool.ts
│ │ ├── GetPropertyLeadsTool.ts
│ │ ├── GetPropertyTool.ts
│ │ ├── ListPropertiesTool.ts
│ │ ├── SearchClientsTool.ts
│ │ ├── UpdateClientTool.ts
│ │ ├── UpdateOwnerTool.ts
│ │ └── UpdatePropertyTool.ts
│ └── index.ts # Server entry point
├── python_tester/ # Python client for testing
│ ├── openai2mcp_test.py # OpenAI chatbot client
│ └── requirements.txt # Python dependencies
├── package.json
├── tsconfig.json
├── .env # Environment variables (create this)
└── README.md
Python Testing Client
The python_tester folder contains a Python script that connects to the MCP server and allows you to interact with it using an OpenAI-powered chatbot.
Setup
-
Ensure the MCP server is running (see step 4 above)
-
Install Python dependencies:
cd python_tester pip install -r requirements.txt -
Run the chatbot:
python3 openai2mcp_test.py
How It Works
The Python client:
- Connects to the MCP server via Streamable HTTP on
http://localhost:1337/mcp - Uses the OpenAI API (GPT-4) to process natural language queries
- Automatically discovers and calls available MCP tools
- Provides a console-based chat interface
Example Usage
You: List all available properties
AI: [Calls list_properties tool and returns results]
You: Get information about property with reference ABC123
AI: [Calls get_property tool with the reference and returns property details]
You: What types of properties are available?
AI: [Calls get_enum tool for property types and returns the list]
Type quit or exit to end the chat session.
Available Tools
The MCP server provides the following tools for interacting with the Inmovilla API:
Properties
create_property- Create a new property or prospectget_property- Get property details by code or referenceget_property_extra_info- Get extra information (portal publication info, leads)get_property_leads- Get leads for properties within a date rangelist_properties- List all propertiesupdate_property- Update an existing property
Clients
create_client- Create a new clientget_client- Get client details by codesearch_clients- Search for clientsupdate_client- Update an existing clientdelete_client- Delete a client
Owners
create_owner- Create a new ownerget_owner- Get owner detailsupdate_owner- Update an existing ownerdelete_owner- Delete an owner
Enumerations
get_enum- Get enum values for various categories (calidades, tipos, paises, ciudades, zonas)
Development
Watch Mode
To automatically rebuild on file changes:
npm run watch
Adding New Tools
Tools are automatically loaded from the src/tools/ directory. Each tool extends the MCPTool class and defines:
name- Tool identifierdescription- What the tool doesschema- Input parameters using Zodexecute()- Tool implementation
API Documentation
For detailed information about the Inmovilla API endpoints and parameters, refer to Documentación API REST v1.html in the project root.
License
[Your License Here]
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。