Commercial real estate cap rate benchmarks by asset class, market tier, and geography. Source: CBRE and JLL quarterly cap rate surveys. Used by CRE acquisition teams, asset managers, and real estate CFOs for property pricing and portfolio valuation.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| asset_class | Yes | ||
| 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 covered. The description adds context about the data source (CBRE/JLL quarterly surveys) and intended audience, but does not disclose other behavioral traits such as output format, data recency, or geographic limitations, which are not addressed elsewhere.
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 two sentences. The first states the tool's purpose; the second provides source and intended users. No filler or redundant content, and it is front-loaded with the essential information.
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 simple read-only benchmark with three enum parameters and no output schema, the description provides adequate context: what the tool measures, data source, and use cases. It does not explicitly state that 'geography' is limited to US regions or what the output looks like, but these are minor gaps given the schema 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?
Schema description coverage is 0%, so the description is the only source. It lists the dimensions (asset class, market tier, geography) which maps to the schema parameters, but it does not add significant meaning beyond the parameter names and enums. The enums are mostly self-explanatory, but the description does not clarify relationships or provide guidance on combining filters.
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 'commercial real estate cap rate benchmarks' by asset class, market tier, and geography, mapping directly to its three parameters. It distinguishes from sibling benchmark tools by focusing specifically on cap rates, unlike get_construction_cost_benchmark or get_cre_debt_benchmark.
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?
It identifies target users (CRE acquisition teams, asset managers, real estate CFOs) and use cases (property pricing, portfolio valuation), providing clear context for when to use this tool. It does not explicitly name alternatives or list exclusions, but the context is sufficient for a specialized benchmark lookup.
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.