NDI-MCP-Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NDI_API_KEY | Yes | Your API key for accessing Northeast Deal Intel services. Sign up at northeastdealintel.com/agent-api.html to get a key. | |
| NDI_API_BASE | No | The base URL for the API. Override if needed. | https://api.northeastdealintel.com |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_dealsA | Search active commercial real estate listings across the Northeast. Returns AI-scored deals with cap rates, pricing, green/red flags, and sell signals. Use this to find deals matching an investor's criteria, scout a submarket, or identify opportunities for a 1031 exchange. |
| get_dealA | Get full details for a specific deal by ID, including complete AI scoring breakdown, green/red flags, sell probability signal, distress tier, and 1031 suitability. |
| search_compsA | Search 100,000+ closed commercial transactions for comp data. Use to benchmark a deal's price/SF or cap rate against actual recent sales. Requires agent_starter tier. |
| score_dealA | Submit a deal for AI scoring. Returns a 1-10 score, green flags, red flags, market benchmarks, and investment thesis. Use when evaluating a deal not yet in the NDI database. Requires agent_pro tier. |
| get_market_benchmarksA | Get cap rate and price/SF benchmarks for a state and property type, derived from closed comps. Use to determine if a deal is priced above or below market. |
| find_1031_candidatesA | Find deals suitable for a 1031 exchange. Filters for income-producing properties with clean structures, appropriate price bands, and NNN/NN lease profiles. Pass the exchanger's target price range and timeline for best results. |
| get_sell_signalA | Get the sell probability signal for a listed property — the likelihood it transacts in the next 6 months based on days-on-market, ownership age, distress tier, and score. High sell signal = motivated seller, potential to negotiate. Requires agent_starter tier. |
| get_market_summaryB | Get a market summary for a state: total active listings, score distribution, average cap rate, deal count by property type, and top submarkets. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Every tool has a clearly distinct purpose with no ambiguity. For example, find_1031_candidates focuses on filtering for 1031 exchange suitability, while search_deals is a broader search for active listings, and get_deal retrieves detailed information for a specific deal. The descriptions reinforce these unique roles, ensuring agents can easily differentiate between them.
All tool names follow a consistent verb_noun pattern using snake_case, such as find_1031_candidates, get_deal, and search_comps. This uniformity makes the toolset predictable and easy to navigate, with no deviations in naming conventions across the eight tools.
With 8 tools, the count is well-scoped for a commercial real estate analytics server. Each tool serves a specific function, from searching and scoring deals to retrieving market data and benchmarks, ensuring comprehensive coverage without unnecessary bloat or gaps.
The toolset provides strong coverage for commercial real estate analysis, including deal discovery, scoring, benchmarking, and market insights. A minor gap exists in update or management operations for deals, but the core workflows for evaluation and research are fully supported, allowing agents to perform essential tasks effectively.