Skip to main content
Glama

AVM long-term-rent estimate with comparables

avm_rent
Read-onlyIdempotent

Run RentCast's automated model for long-term (12-month lease) rent and return a point rent estimate, a rentRangeLow/rentRangeHigh band, the resolved subjectProperty, and comparables[] — recent comparable rentals each scored with distance and correlation. Same subject-pinning rules as avm_value (address or latitude+longitude, plus optional subject attributes). Cached 6h upstream.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressNoSubject property address. Required unless latitude+longitude are given. e.g. "5500 Grand Lake Dr, San Antonio, TX, 78244".
daysOldNoMax days since comparable rentals were last seen (min 1).
bedroomsNoSubject bedroom count; 0 indicates a studio.
latitudeNoSubject latitude; alternative to address (requires longitude).
bathroomsNoSubject bathroom count; supports fractions.
compCountNoNumber of comparable rentals to use in the estimate. Default 15.
longitudeNoSubject longitude; alternative to address (requires latitude).
maxRadiusNoMax distance between comparables and subject, in miles.
propertyTypeNoSubject property type; improves comparable selection.
squareFootageNoSubject living-area square footage.
lookupSubjectAttributesNoWhen true, RentCast looks up any missing subject attributes from its records. Default true.

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond these: it specifies the output schema (point estimate, band, subject property, comparables), notes that comparables are scored by distance and correlation, mentions 'Cached 6h upstream', and references deterministic subject-pinning rules. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded: the first sentence states the primary purpose and outputs, the second clarifies subject pinning, and the third notes caching. Every sentence contributes essential context without excessive detail. It is well-structured and avoids redundancy with the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having 11 parameters and no output schema, the description provides a useful summary of return values (point estimate, range band, subjectProperty, comparables with scores) and mentions key behavior like caching and subject-pinning rules. It does not repeat parameter details already covered by the schema. A minor gap is not explicitly mentioning default behaviors like compCount default, but those are documented in the schema, so overall completeness is strong.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-level semantics beyond what the schema already provides; it only restates that address or latitude+longitude can be used and that subject attributes are optional. No additional param details or examples are given in the description, so it neither compensates nor detracts from the schema's coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Run RentCast's automated model for long-term (12-month lease) rent' and enumerates the specific outputs (point estimate, rentRangeLow/rentRangeHigh, subjectProperty, comparables). This distinguishes it from sibling avm_value (which is likely a sale value estimate) and other search tools by focusing on a long-term rent AVM with comparables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by calling it 'long-term rent estimate' and referencing 'Same subject-pinning rules as avm_value', indicating it is the rent counterpart to avm_value. It also clarifies the need for address or latitude+longitude and optional attributes. However, it does not explicitly state when to prefer this over alternatives like search_rental_listings, nor does it provide exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: avm_rent and avm_value target rent vs. property value, while search_properties, search_rental_listings, and search_sale_listings each address different record types (property records, rental listings, sale listings). get_property is the only single-record retrieval tool. No overlap or ambiguity exists.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern: avm_rent/avm_value, search_properties/search_rental_listings/search_sale_listings, and get_property. The naming is predictable and uniformly formatted with action prefixes and clear resource targets.

Tool Count5/5

With 6 tools, the set is well-scoped for a real estate data server, covering property search, property detail, valuation models, and both rental and sale listings. Each tool earns its place without redundancy or excessive granularity.

Completeness5/5

The tool surface comprehensively covers the domain: property search with detailed records, single-property lookup, AVM for both rent and sale value, and listing search for both rental and sale. The read-only nature of the API means no CRUD operations are expected, and no obvious gaps remain for core workflows.