Rental market benchmarks — asking rents by unit type, live vacancy rate from FRED, rent growth trends, and rent-to-income ratios by market tier. Sources: HUD Fair Market Rents, FRED live vacancy, ApartmentList public data. For landlords, multifamily investors, and property managers.
| Name | Required | Description | Default |
|---|---|---|---|
| unit_type | No | ||
| market_tier | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds useful context about data sources (HUD, FRED, ApartmentList) and the live vacancy rate, but does not disclose any additional behavioral traits such as rate limits or response structure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the main output types and then provide sources and target audience. No redundant wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a read-only benchmark lookup with optional parameters, the description adequately lists the output types and sources. It doesn't describe return format or pagination, but that's less critical given the simple parameter set and annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description mentions 'by unit type' and 'by market tier', which correspond to the two schema parameters. However, it doesn't explain the meaning of specific enum values or how parameters interact, and with 0% schema description coverage, more detail would be helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides rental market benchmarks including asking rents, vacancy rates, rent growth, and rent-to-income ratios. It doesn't explicitly distinguish from sibling tools like get_residential_market_benchmark, so it lacks sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The target audience is specified (landlords, investors, property managers), but there is no explicit guidance on when to use this tool versus alternatives like get_hud_fair_market_rent or get_residential_market_benchmark. The usage context is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a specific real estate metric or data source, with detailed descriptions and examples that clearly differentiate them. Even the four climate-related tools serve distinct purposes: composite risk, macroeconomic losses, historical storm tally, and short-term weather scheduling risk. The only potential overlap is between get_noaa_disaster_economics and get_storm_event_history, but their descriptions clarify different use cases.
All 19 tools follow the exact same 'get_' prefix with lowercase snake_case descriptive suffixes. No mixed conventions, no irregular verbs, completely predictable pattern. This makes the tool names easy to learn and reliably distinguishable.
19 tools is on the higher end for a data retrieval server, but the domain encompasses pricing, rents, costs, debt, climate, development, and market metrics, justifying a broad catalog. The four climate tools could arguably be consolidated, but each has a distinct use case and data source, making the count reasonable for a comprehensive real estate benchmark server.
The server covers the core real estate lifecycle: acquisition (cap rates, climate risk), development (construction costs, pro forma), financing (debt benchmarks, mortgage rates), operation (property operating, tax), and market analysis (supply, rents, residential, REITs, NCREIF). Minor gaps exist such as transaction volume data or sub-market specific leasing indicators, but the coverage is extensive for benchmark-oriented use cases.