Live mortgage rate benchmarks — 30Y and 15Y fixed from FRED weekly survey, ARM spreads, points and fees, DTI standards, and affordability index. For homebuyers, lenders, real estate agents, and housing analysts. Rates update weekly.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | ||
| loan_type | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds that the data is 'live' and updates weekly, sourced from FRED, which is useful behavioral context beyond the annotations without contradicting them.
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 a single, information-dense sentence that front-loads the core purpose ('Live mortgage rate benchmarks') and then lists specific metrics in a compact list. The second sentence clarifies audience and update cadence. Every phrase earns its place, with no redundant wording.
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 outlines the data content and audience but omits any mention of the optional state and loan_type parameters, leaving the agent uncertain about request customization. No output schema exists, and the description does not explain return format or pagination, though the listed content gives some intuition.
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%, meaning the description provides no explanation of the 'state' or 'loan_type' parameters. The schema itself lists 'state' as a generic string and 'loan_type' as an enum, but the description does not indicate whether these are filters, how to format state values, or what impact they have on results. This is a significant gap.
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 'Live mortgage rate benchmarks' and enumerates specific components (30Y/15Y fixed, ARM spreads, DTI standards, etc.), which distinguishes it from sibling benchmark tools for other asset classes. The verb 'get' frames it as a retrieval operation, and the focus on mortgage-specific data separates it from cap rates, debt, or rental benchmarks.
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 the target audience ('homebuyers, lenders, real estate agents, housing analysts') and notes weekly updates, providing some context. However, it does not explicitly state when to use this tool versus alternatives like get_cre_debt_benchmark, nor does it offer exclusions or guidance for selecting between the many sibling benchmark tools.
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.