CRE debt stress benchmarks — live delinquency rate from FRED, CMBS delinquency by property type, maturity wall exposure, and stressed cap rate scenarios. For lenders, special servicers, distressed investors, and regulators. Delinquency rate updates quarterly.
| Name | Required | Description | Default |
|---|---|---|---|
| scenario | No | ||
| property_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 useful behavioral context: data sources (FRED, CMBS) and a quarterly update note. However, it doesn't disclose response behavior, rate limits, or how scenario choices affect output, which would be valuable beyond the 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 three concise sentences: the first states the core offering, the second names the audience, the third gives a key update frequency. Every sentence earns its place with no redundancy or filler.
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?
With only 2 optional parameters and no output schema, the description provides a reasonable high-level overview of the data included. However, it does not explain how parameters filter the output, what the return format might be, or the significance of terms like 'maturity wall exposure' and 'stressed cap rate scenarios', leaving some ambiguity for an agent deciding if this tool fits the need.
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 coverage is 0%, so the description must compensate. It indirectly references the parameters with 'stressed cap rate scenarios' and 'by property type', but does not explain the enum values (base/stress/severe_stress) or what each property type includes. This leaves significant semantic gaps for an agent.
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 CRE debt stress benchmarks, listing specific components (delinquency rate from FRED, CMBS delinquency by property type, maturity wall exposure, stressed cap rate scenarios). This specific detail distinguishes it from siblings like get_cre_debt_benchmark and get_cap_rate_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?
The description names target users (lenders, special servicers, distressed investors, regulators) but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or comparisons to sibling tools are provided, so guidance is implied rather than explicit.
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.