fetchGet the full Dim Hour record for one venue by the id returned from search: description, signature dishes, address, hours, phone, happy hour, reservation platform, awards, website and Instagram.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Venue id from `search`, in the form 'city:id' e.g. 'nyc:1367' |
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| url | Yes | ||
| text | Yes | ||
| title | Yes | ||
| metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare read-only and non-destructive behavior. The description adds value by enumerating the response fields (description, signature dishes, address, hours, phone, happy hour, reservation platform, awards, website, Instagram) and indicating a comprehensive retrieval. It does not cover error cases but that is acceptable given the simple read-only nature.
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, well-structured sentence that front-loads the action and resource. The list of returned fields is informative without being verbose. Every phrase contributes to understanding the tool's purpose and output.
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 tool's simplicity (one parameter), the existing annotations, and the presence of an output schema, the description provides sufficient context for an agent to select and invoke it correctly. It clearly states the input source and the nature of the returned data.
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 schema already provides a thorough description of the `id` parameter, including its source (from search) and an example format. The description reiterates this source but adds no new semantic detail. Since schema coverage is 100%, a baseline score of 3 is appropriate.
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 ('Get') and resource ('full Dim Hour record'), and clearly states the input source (id from search). It lists the contained fields, making the purpose unambiguous. However, it does not explicitly differentiate itself from the sibling tool 'get_venue', so it misses the top score.
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 a clear workflow: first call `search` to obtain an id, then use this tool to fetch the full record. This provides useful context for when to use it. It does not explicitly mention alternatives or when not to use it, so it stops short of giving full exclusions.
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.
There are two pairs of nearly identical tools: `fetch` and `get_venue` both retrieve full venue records by ID, differing only in an optional long-form story; `search` and `search_venues` both perform catalog searches with overlapping scope and filtering. This creates real ambiguity about which tool to select for a given task.
Naming mixes single verbs (`fetch`, `search`) with verb_noun snake_case (`get_venue`, `list_cities`, `search_venues`). The pattern is not uniform, and synonyms like fetch/get and search/search_venues further muddy the naming convention.
Seven tools is a reasonable number for a venue discovery server. Even accounting for redundancy, the count is well within the typical range and each tool ostensibly serves a distinct operation (search, retrieve, list).
The tool surface covers the core workflows: discovering venues (search, search_venues), retrieving details (fetch, get_venue), and listing collections (list_cities, list_curated, list_new_venues). No major lifecycle operations are missing, though the redundant pairs suggest the design could have been more streamlined.