get_methodologyHow every figure is defined and computed: the glossary, the provenance of each source, and the short-let cost-model constants with their live values. Read it before quoting any figure. Yields are gross, rents are registered rather than asking, and short-let occupancy is a stated assumption — never a measurement.
| Name | Required | Description | Default |
|---|---|---|---|
| term | No | One definition; omit for the whole glossary. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses critical interpretive caveats: yields are gross, rents are registered rather than asking, and short-let occupancy is an assumption. These prevent the agent from misquoting figures, adding real behavioral context.
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 compact and front-loaded with the core purpose, followed by usage and caveats. Every sentence earns its place, with no filler or redundancy.
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 one-optional-parameter glossary tool, the description fully covers what the tool returns, why it matters, how it should be used, and the key interpretive constraints. Nothing essential is missing.
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 100%, so the schema already explains the 'term' parameter and its enum. The description adds no new parameter-level detail, but that is acceptable given the high schema coverage; the baseline of 3 applies.
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 the source for how figures are defined and computed, covering glossary, provenance, and constants. This distinguishes it sharply from sibling data-lookup tools like get_area or get_building, which return figures rather than explain them.
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 instruction 'Read it before quoting any figure' explicitly states when to use it. While it does not name alternatives or exclusions, the tool's role as a methodology reference makes alternatives largely irrelevant, so the guidance is adequate.
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 distinct resource and action: fetch retrieves any full record by id or URL, get_area handles area-level medians, get_building gives building records, get_building_transactions lists underlying transactions, get_city_overview provides citywide scope, get_methodology explains definitions, get_ranking returns a single sorted ranking, get_villa_community focuses on villa communities, list_favorites accesses account-specific data, list_rankings enumerates available rankings, and search resolves names to ids. No two tools have overlapping purposes, even when addressing similar domains (e.g., list_rankings vs. get_ranking are clearly differentiated by singular vs. plural and availability of methods).
All tool names use a consistent lowercase_with_underscores format and follow a clear verb_noun pattern: get_ for retrieving specific entities, list_ for enumerations, search for lookup, and fetch for generic retrieval. This makes the API predictable and easy to navigate.
With 11 tools, the server is well-scoped for a real estate index covering city, areas, buildings, villas, transactions, rankings, methodology, and account favorites. Each tool serves a unique function without unnecessary redundancy, fitting the typical range for a purpose-built data server.
The tool set provides comprehensive coverage for a read-only data server: search, area and building details, transaction histories, citywide overview, rankings and their methods, villa communities, methodology reference, and saved favorites. There are no obvious gaps—users can navigate from search to specific records, drill into transactions, compare rankings, and retrieve the methodology behind any figure.