REIT valuation and performance benchmarks — FFO multiples, AFFO multiples, dividend yields, NAV premium/discount, and total returns by property sector. Source: NAREIT public monthly data. For REIT analysts, portfolio managers, and IR teams. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| property_sector | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds context about the data source (NAREIT monthly data) and that it is free, which is useful. However, it does not disclose return format, historical depth, or potential limitations, so behavioral transparency is only moderately enhanced.
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?
The description is three sentences with the purpose front-loaded. It efficiently covers the tool's function, data source, and intended users. The 'Free' is slightly extraneous but does not detract significantly, making it concise without being overly terse.
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?
For a read-only tool with one parameter and no output schema, the description covers core purpose, audience, and source. However, it omits details about what the response contains (e.g., a single value vs. a time series) and any data range restrictions, which leaves some ambiguity for an agent invoking the tool.
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 says 'by property sector' but does not list the allowed enum values, even though schema description coverage is 0%. Since there is only one parameter and it is self-explanatory from the name, the description adds minimal value beyond the schema. The agent would need to inspect the enum to know valid sectors.
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 REIT valuation and performance benchmarks, listing specific metrics like FFO multiples, AFFO multiples, dividend yields, NAV premium/discount, and total returns by property sector. This distinguishes it from sibling benchmark tools, which focus on other real estate metrics.
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 description identifies the target audience (REIT analysts, portfolio managers, IR teams) and implies the tool is for obtaining REIT-specific benchmark data. It does not explicitly name alternatives or when-not-to-use cases, but the context is clear enough for an agent to select this tool over sibling benchmarks.
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.