Live housing supply indicators — starts, permits, completions, and absorption by market tier from FRED and Census. Leading indicator for housing prices 6-12 months ahead. For developers, lenders, investors, and housing policy analysts.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| structure_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds that the data is 'live' and sourced from FRED/Census, but it does not disclose response structure, update frequency, or limitations. With the annotations covering the safety profile, this is adequate but not especially rich.
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 and front-loads the core function, then adds application and audience. It is appropriately sized with no filler, though the final sentence about target audience is somewhat promotional.
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 tool with two optional enum parameters and no output schema, the description gives high-level context (indicators, source, use case) but omits return format and parameter guidance. It is minimally complete but leaves the agent to infer output structure from the tool name and general knowledge.
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%, and the description does not explain the 'region' or 'structure_type' parameters. It even mentions 'by market tier,' which does not directly correspond to the provided enums (geographic regions and structure types), creating potential confusion. The description fails to compensate for the schema's lack of parameter descriptions.
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 resource: 'Live housing supply indicators — starts, permits, completions, and absorption' and names data sources (FRED and Census), which distinguishes it from sibling benchmark tools. It lacks an explicit verb like 'retrieves,' but the 'get_' prefix and concrete indicator list make the purpose evident.
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 provides clear usage context by stating the tool is 'a leading indicator for housing prices 6-12 months ahead' and names target audiences (developers, lenders, investors, housing policy analysts). It does not explicitly mention when not to use it or compare it to alternatives like get_residential_market_benchmark, so a small gap remains.
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.