Green Street MCP. Analyze CRE data and market trends in one chat.
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Green Street. Manage commercial real estate (CRE) and REIT data. Your AI client pulls primary financials, market grades, transaction history, and news articles directly from Green Street.
List companies, retrieve specific metrics like NAV and FFO/FAD, and get market projections. It's a full-spectrum CRE intelligence feed for investment analysts and asset managers.
What your AI agents can do
Get company summary
Gets the basic financial summary for a specific company using its stock symbol.
Get earnings metrics
Retrieves FFO/FAD earnings data for a company by symbol.
Get forecast scenarios
Gets forward-looking market and sector projections for a given area.
List all available REITs and real estate companies, then pull a financial summary for a specific company symbol.
Get specific earnings data (FFO/FAD) or Net Asset Value (NAV) estimates for any company.
Get market-level grades and sector analytics to understand competitive rankings across property types.
Retrieve forward-looking projections, including NOI forecasts, for entire markets or sectors.
Break down a REIT portfolio to see its geographic and property-type distribution.
Pull historical transaction summaries and search the latest commercial real estate news articles.
Ask AI about this MCP
Supported MCP Clients
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019d75abget company summary
Gets the basic financial summary for a specific company using its stock symbol.
019d75abget earnings metrics
Retrieves FFO/FAD earnings data for a company by symbol.
019d75abget forecast scenarios
Gets forward-looking market and sector projections for a given area.
019d75abget historical transactions
Retrieves summaries of past real estate transactions for specific properties or areas.
019d75abget market grades
Gets the official market grade and ranking for a specified market area.
019d75abget market projections
Gets forward-looking Net Operating Income (NOI) projections for an entire market.
019d75abget market sector summary
Gets analytics and grades for a specific commercial real estate sector.
019d75abget nav estimates
Gets the estimated Net Asset Value (NAV) for a specific company.
019d75abget news articles
Searches and pulls the latest commercial real estate news and articles.
019d75abget portfolio breakout
Shows the geographic and property-type breakdown for a specific company's portfolio.
019d75ablist companies
Lists every REIT and real estate company covered by Green Street's data set.
019d75ablist sectors
Lists all available commercial real estate sectors to define your scope.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with Green Street, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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What you can do with this MCP connector
Green Street MCP Server: CRE Intelligence
Your AI client pulls primary financials, market grades, transaction history, and news articles directly from Green Street. You can use this server to run deep, multi-step CRE analysis without jumping between data platforms.
List and Summarize Companies
You can start by using list_companies to see every REIT and real estate company covered by Green Street's data set, and then you can run get_company_summary to pull a basic financial summary for any specific company symbol.
Retrieve Financial Metrics
To get specific earnings data, you can use get_earnings_metrics for FFO/FAD data by company symbol, and you can get estimated Net Asset Value (NAV) with get_nav_estimates for any company.
Analyze Market Rankings
You can check a market's official grade and ranking using get_market_grades, and you can get deep sector analysis and grades for a specific commercial real estate sector via get_market_sector_summary.
Forecast Market Trends
For forward-looking projections, you can pull Net Operating Income (NOI) forecasts for an entire market using get_market_projections, and you can also get forward-looking market and sector projections using get_forecast_scenarios.
Audit Portfolio Exposure
You can see a company's portfolio broken down by geography and property type using get_portfolio_breakout, and you can track past real estate deals by running get_historical_transactions to pull summaries of transactions for specific properties or areas.
News and Market Context
You can search for the latest CRE news and articles with get_news_articles, and you can use list_sectors to see all the commercial real estate sectors available to define your scope.
How Green Street MCP Works
- 1 Subscribe to the Green Street server and provide your Client ID and Client Secret.
- 2 Start a conversation with your AI client (Claude, Cursor, etc.) and ask a question (e.g., 'What is the market grade for New York Office?').
- 3 Your agent executes the necessary tools, pulls the raw data, and synthesizes the answer into a plain language report for you.
The bottom line is, you talk to your agent like a human analyst, and it handles all the underlying data calls and formatting.
Who Is Green Street MCP For?
Investment analysts and asset managers need this. If you spend your day cross-referencing quarterly reports, comparing market grades, and synthesizing news feeds from a dozen different sites, this saves you hours. It puts all that deep market intelligence right into your chat window.
Pulls NAV estimates and earnings metrics for REITs during underwriting, then cross-references those figures with current market grades.
Monitors market grades and sector analytics to optimize portfolio positioning. They use the agent to audit exposure against forward-looking NOI projections.
Retrieves historical transaction summaries and market news directly through the chat interface to support client pitches.
What Changes When You Connect
- Understand a company's core financials and market standing immediately. Use
get_company_summaryandget_nav_estimatestogether to quickly assess a target for underwriting. - Benchmark entire markets and sectors. Run
get_market_gradesagainstget_market_sector_summaryto see where the best assets are performing relative to their category. - Plan for the future instead of reacting to the past. Pull forward-looking insights using
get_market_projectionsorget_forecast_scenariosto build out a strategic roadmap. - Audit risk and exposure. Use
get_portfolio_breakoutto map a company's holdings, then cross-reference that againstget_market_gradesto find concentrated risk areas. - Stay ahead of the news cycle. Use
get_news_articlesto pull recent market commentary, which you can then pair withget_earnings_metricsto gauge market reaction to earnings. - Manage the whole lifecycle. Start by calling
list_sectorsto define your scope, then uselist_companiesto pull the relevant players, and finally runget_company_summaryon the list.
Real-World Use Cases
Assessing a new investment target
You've identified a potential REIT. Instead of visiting four separate reports, you ask your agent to run get_company_summary and get_nav_estimates for the symbol. Next, you ask the agent to check the market context using get_market_grades and get_market_sector_summary. This gives you a complete, multi-layered investment picture in minutes.
Auditing portfolio risk
Your client needs to know if their portfolio is too concentrated. You ask the agent to use get_portfolio_breakout to map the geography and property types. You then use get_market_grades on the top exposed markets to flag immediate risk areas.
Understanding market shifts
A sector is struggling. You ask the agent to run get_news_articles for the last month. Then, you use get_market_projections to see if the market's NOI forecast adjusts downward. This links qualitative market sentiment to quantitative financial risk.
Building a competitive landscape report
You need to compare three different companies. First, you use list_companies to get the full roster. Then, you run get_company_summary and get_earnings_metrics for all three. Finally, you use get_market_sector_summary to provide a common competitive benchmark.
The Tradeoffs
Looking up data piece by piece
Asking your agent, 'What is the market grade?' in one chat, and then starting a new chat to 'Get the company summary.' This forces you to switch context and makes the final report hard to stitch together.
→ Keep all your questions in one thread. Start by asking the agent to 'Compare the market grade for the NY office market with the financial summary of PLD.' This ensures the agent runs all necessary tools and presents a single, cohesive report.
Forgetting the time element
Asking for current market grades without specifying the timeframe. The agent might give you outdated data, making your analysis unreliable.
→
Always ask for the most recent data available. When comparing metrics, specify the period (e.g., 'Compare FFO/FAD for Q4 2023 vs. Q4 2022'). This uses the precision of get_earnings_metrics and get_market_grades.
Ignoring the whole picture
Only running get_company_summary on a single company and calling it a complete analysis. You miss the market context and risk factors.
→
Always frame the company data against the market. Use get_company_summary and then immediately follow up with get_market_sector_summary to put the company into its proper competitive context.
When It Fits, When It Doesn't
Use this server if you need to build a multi-layered investment thesis. This means you need to connect a company's fundamentals (get_company_summary, get_nav_estimates) to the macro market context (get_market_grades, get_market_projections). If your job involves risk assessment, valuation, or competitive benchmarking, this is your tool. Don't use it if you only need one piece of information (e.g., just checking a single news headline). For that, a simple search engine is faster. This server is for synthesizing complex, interconnected data points.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Green Street. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 12 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Cross-referencing CRE data used to be a nightmare of tabs and PDFs.
Today, you pull up Bloomberg, then you open the sector report PDF, then you find the company's annual filing, and finally, you copy-paste the relevant market grade from a specialized industry site. You spend hours just trying to get four pieces of related data into one document, risking mismatched dates and broken links.
With Green Street, you just talk to your agent. You ask, 'What is the market grade for the Industrial sector, and what is PLD's NAV estimate?' The agent runs `get_market_grades` and `get_nav_estimates`, pulls both data sets, and gives you a single, formatted answer.
Green Street MCP Server: Full Control Over CRE Data
You no longer have to manually search for transactions or market news. The agent uses `get_historical_transactions` to pull structured transaction summaries and `get_news_articles` to pull the latest market commentary. You get both the hard numbers and the qualitative context, instantly.
The difference is that you're not just gathering data; you're building a complete, traceable intelligence report in a single, conversational flow. It’s everything you need, right here.
Common Questions About Green Street MCP
How do I use the get_market_grades tool? +
You ask the agent for the market grade, specifying the market area (e.g., 'New York Office market'). The agent runs get_market_grades and returns the current rating and what that rating reflects.
Can I use get_company_summary to compare multiple REITs? +
Yes. You ask the agent to list the companies first using list_companies, and then request a summary for a list of symbols. The agent compiles the data from get_company_summary for all specified symbols.
What is the difference between get_market_projections and get_forecast_scenarios? +
They both deal with future data, but they cover different things. get_market_projections focuses on NOI forecasts for an entire market, while get_forecast_scenarios gives broader, forward-looking market and sector projections.
Does the get_news_articles tool cover all CRE news? +
The tool searches and pulls curated commercial real estate news. You simply ask the agent to 'Find recent news about the Industrial sector.' The agent handles the search and retrieval.
How do I use the get_portfolio_breakout tool? +
You pass a company's ticker symbol. The tool returns a detailed breakdown of the portfolio, showing its geographic and property-type exposure. This helps you audit where a REIT's assets are concentrated.
What information does get_earnings_metrics provide? +
It provides specific financial data like Funds From Operations (FFO) and Funds From Adjusted Distributions (FAD). These metrics are key for analyzing a company's operational earnings performance.
Can I use list_companies to find a specific sector? +
No, list_companies lists all covered REITs and companies. To filter by sector, you should first use list_sectors to get the available sector names, and then reference that list when querying a specific company.
Are there limits to using the get_market_grades tool? +
The tool has standard rate limits managed by Vinkius. If you hit a limit, your AI client will receive a clear error message, indicating when you can try again.
Can my agent retrieve NAV estimates for a specific REIT in Green Street? +
Yes. Use the 'get_nav_estimates' tool. The agent will fetch Net Asset Value estimates, helping you evaluate listed companies against their private market valuations flawlessly.
How do I access forward-looking market projections via chat? +
Use the 'get_market_projections' tool. Your agent will retrieve NOI forecasts and sector growth projections generated by Green Street's advisory teams, providing deep strategic foresight flawlessly.
Can I search for recent news related to specific property sectors through the agent? +
Absolutely. Use the 'get_news_articles' tool. Provide a keyword or sector (e.g., 'Industrial' or 'Office'). The agent will perform a scan across curated commercial real estate news and return ranked excerpts natively.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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