Construction cost benchmarks — hard cost per SF by building type and region, soft cost ratios, contingency standards, and live material cost escalation signals. Sources: NAHB, Turner Building Cost Index, RSMeans composites. For developers, lenders, and project owners.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| building_type | Yes | ||
| construction_class | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, and the description adds the data sources and content types. However, it does not disclose additional behavioral traits such as update frequency, data interpretation nuances, or any operational caveats, leaving the description to carry only modest extra context beyond annotations.
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 concise and front-loaded, with the primary function clearly stated first. The source list and target audience add value without redundancy, resulting in an efficient and well-structured message.
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?
No output schema exists, so the description carries the burden of explaining the return value. It lists the types of benchmarks and sources, which gives a good overview, but lacks detail on how construction_class influences output and what the result structure looks like. With the readOnly annotation, this is fairly complete but not exhaustive.
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?
With 0% schema description coverage, the description partially compensates by mentioning 'building type and region' for hard cost per SF, but does not explain the 'construction_class' parameter or its relationship to the output. The enums provide options, but their significance is not clarified, leaving a gap in parameter understanding.
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 identifies the tool's purpose: providing construction cost benchmarks with specific components (hard cost per SF, soft cost ratios, contingency standards, and material cost escalation signals). It distinguishes itself from sibling benchmark tools by focusing on construction costs.
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 implies usage for construction cost estimation, naming target audiences (developers, lenders, project owners), but does not explicitly state when to use this over other benchmark tools or mention any exclusions/alternatives.
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.