Property tax benchmarks — effective tax rates by state and property type, assessment ratios, and appeal success rates. Source: Lincoln Institute of Land Policy. For property owners, asset managers, and acquisition teams. Property tax is the largest controllable operating expense for most commercial properties.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter US state code | |
| property_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe read-only operation. The description adds context about the data source and the specific metrics returned. It doesn't mention response format or error handling, but given the read-only nature, this is acceptable.
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?
Three sentences, no fluff. The first sentence is highly informative, the second gives source credibility, and the third provides practical context. Every sentence earns its place.
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?
The description covers the core output metrics and data source. It doesn't specify that state is required and property_type is optional (schema handles that), nor does it describe response format. Given the simple two-parameter query and read-only annotation, this is nearly complete.
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 covers 'state' with a description, but 'property_type' lacks one. The description links both parameters to the output ('by state and property type'), partially compensating. However, it doesn't clarify that property_type is optional, and the enum values are self-explanatory.
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 as providing property tax benchmarks, listing specific metrics (effective tax rates, assessment ratios, appeal success rates) and the resource (Lincoln Institute data). This distinguishes it from sibling benchmark tools such as cap rate or construction cost.
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 names the intended audience (property owners, asset managers, acquisition teams) and gives context about property tax being the largest controllable operating expense. This provides clear usage context, though it does not explicitly mention alternatives or when not to use the tool.
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.