Tenant Leasing Analytics
Enables analysis of prospective tenant inquiries and market rent comparisons through database queries, guest card analytics, and automated generation of leasing emails and visual market reports with charts.
README
Tenant Leasing Analytics - MCP Server
A specialized MCP (Model Context Protocol) server for tenant leasing analytics, focused on guest card management and market rent comparisons.
📊 Data Architecture
This MCP server only uses data from the tenant-info/ folder:
| Table | Description | Rows |
|---|---|---|
guest_cards |
Prospective tenant inquiries with preferences | 100 |
nearby_units |
Comparable rental listings in the area | 100 |
🛠️ Available Tools
Schema & Query
| Tool | Description |
|---|---|
get_schema() |
View database schema and column descriptions |
query_database(sql) |
Execute any SELECT query |
Guest Card Analytics
| Tool | Description |
|---|---|
guest_card_summary() |
Comprehensive summary of all inquiries |
qualified_prospects(min_income, min_credit) |
Find prospects meeting criteria |
Market Analytics
| Tool | Description |
|---|---|
market_rent_analysis() |
Analyze nearby rental market conditions |
📧 Email Generation
| Tool | Description |
|---|---|
generate_leasing_email(...) |
Create professional leasing update email |
📊 Visual Reports
| Tool | Description |
|---|---|
create_market_report() |
Full 6-chart visual report (bar, pie, histogram) |
create_individual_chart(type) |
Generate specific chart types |
📧 Email Generation
The generate_leasing_email() tool creates professional leasing update emails like:
Good Morning Chi,
Last week in total we received 17 inquiries and I had no groups confirm showings.
As discussed, we decreased the rate to $2400 and have received 4 new inquiries...
Parameters:
recipient_name: Email recipientsender_name: Your namecurrent_rate: Current advertised rentprevious_rate: Previous rent rateshowings_confirmed: Number of confirmed showingsshowings_attended: Number who attendedinterested_parties: Number who seemed interestedpending_applications: Current pending appswithdrawn_applications: Withdrawn appsupcoming_showings: Scheduled future showings
📊 Visual Reports
Full Market Report (create_market_report())
Generates a comprehensive 6-panel report including:
- Rent Distribution Histogram - Nearby rental price spread
- Credit Score Pie Chart - Prospect credit quality
- Pet Preferences Bar Chart - Pet ownership breakdown
- Budget Distribution Histogram - Prospect max rent budgets
- Price Comparison Bar Chart - Market vs our rate
- Activity Types Pie Chart - Prospect engagement
Individual Charts (create_individual_chart(type))
Available chart types:
rent_histogram- Distribution of nearby rental pricescredit_pie- Credit score distributionpet_bar- Pet preferences breakdownbudget_histogram- Prospect budget distributionprice_comparison- Market vs our pricingactivity_pie- Prospect activity typesincome_vs_rent- Income vs max rent scattersimilarity_rent- Property similarity vs rent scatter
🚀 Setup
Prerequisites
- Python 3.10+
uvpackage manager
Installation
cd /Users/kkamalva/financial_analysis/MCP/kurt-data
uv sync
Claude Desktop Configuration
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"tenant-leasing": {
"command": "/Users/kkamalva/financial_analysis/MCP/kurt-data/run_server.sh"
}
}
}
💬 Example Questions
Guest Card Questions
- "Show me a summary of all guest cards"
- "Find qualified prospects with income over $8,000"
- "What's the credit score distribution of our prospects?"
Market Questions
- "Analyze the nearby rental market"
- "How does our price compare to the market?"
- "What's the average rent in the area?"
Email & Reports
- "Generate a leasing update email for Chi"
- "Create a market report with charts"
- "Generate a rent histogram"
📁 Data Files
This MCP server is self-contained within the kurt-data/ folder and only uses data from tenant-info/:
kurt-data/
├── server.py ← MCP server
├── run_server.sh ← Launch script
├── pyproject.toml ← Dependencies
├── tenant-info/
│ ├── synthetic_guest_cards.csv
│ └── nearby_advertised_units.csv
└── charts/
└── (generated visualizations)
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。
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。
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。
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。