fetchRetrieve the full content of a single Tu Lugar listing by its id (from a search result). Returns title, full text description, specs, and the canonical URL.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Listing id returned by `search`. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true. The description adds value by specifying the return content (title, full text description, specs, canonical URL), which goes beyond the annotation. No behavioral traits like side effects or permissions are needed beyond the read-only hint.
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?
Two sentences with no wasted words. The verb and resource are front-loaded, and each sentence serves a purpose: first defines action, second lists outputs.
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?
Given the lack of an output schema, the description provides reasonable detail about the return value (title, description, specs, URL). It could mention error conditions or rate limits, but for a simple fetch tool this is sufficient.
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 100% (one parameter, fully described). The description reinforces the schema by noting the id comes from a `search` result, providing additional context that helps the agent understand the parameter's origin.
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 uses the specific verb 'Retrieve' and identifies the resource as 'full content of a single Tu Lugar listing', clearly distinguishing it from sibling tools like 'search' (which returns lists) and 'get_listing' (which may have different semantics). The source of the id ('from a `search` result') adds precision.
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 states that the id comes from a search result, implying it is meant to be used after `search`. While it doesn't explicitly exclude other sources or mention alternatives like `get_listing`, the context is clear enough for an AI agent to use correctly.
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.
Most tools have distinct purposes covering account management, listings, search, financials, etc. However, `fetch` and `get_listing` both retrieve full listing details, creating potential confusion. Also, `search` and `search_listings` overlap but are differentiated by free-text vs. filtered search.
All tool names follow a consistent `verb_noun` pattern in snake_case (e.g., `create_listing`, `publish_listing`, `connect_account`). The naming is predictable and clear throughout.
At 39 tools, the server is quite large for a real estate domain. While it covers many aspects, some tools like `fetch` and `get_listing` are redundant. The count feels excessive and could be streamlined to around 25-30 without losing functionality.
The tool set covers listing CRUD, various search types, agent/company profiles, financial calculators, inquiries, saved searches, and account management. Minor gaps exist, such as lack of bulk operations or advanced analytics, but core workflows are well-supported.