Open UniAffitti account areas
get_account_linksReturn deep links for account, listing creation, messages, requests, and favorites in the full UniAffitti web app.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
get_account_linksReturn deep links for account, listing creation, messages, requests, and favorites in the full UniAffitti web app.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation read-only and non-destructive. The description adds useful context about which areas the links cover, but it does not describe the exact return structure, whether the links are absolute URLs, or any authentication context. With annotation coverage present, the added behavioral detail is adequate but not rich.
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 sentence, front-loaded with the action ('Return deep links'), and every phrase adds meaning by enumerating the target account areas. There is no redundant or filler 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?
For a zero-parameter, read-only link-retrieval tool, the description gives enough context: the agent knows exactly what is returned and which areas are covered. The only minor gap is that the return format itself is unspecified, but the absence of an output schema and the simplicity of the tool make this acceptable.
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?
The tool has zero parameters, so the input schema already exhaustively covers invocation needs. Per the baseline rule for 0-parameter tools, the description does not need to add parameter-level detail. No parameter ambiguity exists.
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 a specific verb ('Return deep links') and names the exact resources covered: account, listing creation, messages, requests, and favorites. It clearly distinguishes this from the data-returning sibling tools such as list_my_favorites or get_my_profile. The scope is unambiguous.
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 implies when to use this tool: when the agent needs deep links into those account areas in the full web app, rather than retrieving data directly. However, it does not explicitly state exclusions or mention alternatives, leaving some inference to the agent. A clear usage note versus sibling data tools would have made this stronger.
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 are distinct by resource and action, but the search/catalog cluster is fuzzy: search_universities, search_cities, list_search_metadata, and list_university_campuses overlap in purpose. search_rooms and rank_room_matches also have a similar discovery role, making it easy to select the wrong tool without careful reading.
All tool names follow a consistent snake_case verb_noun pattern, with verbs like add, list, get, search, create, update, and send clearly indicating the action. This makes the tool surface highly predictable and easy to navigate.
20 tools is on the heavier side, but the count is justified by the broad scope: room search, comparison, ranking, favorites, rental requests, messaging, listing creation, and university catalog browsing. The set remains manageable because each tool maps to a distinct workflow step.
Core room-finding workflows are covered well: search, details, compare, favorites, requests, and messages. However, listing management is incomplete (create_listing has no update/delete) and messaging only supports sending a first message, with no tool for replying to an existing conversation, leaving notable dead ends.