resy_add_favorite
Save a restaurant to your Resy favorites using its venue ID for quick access and faster booking.
Instructions
Add a venue to the user's favorites by venue_id.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| venue_id | Yes |
Save a restaurant to your Resy favorites using its venue ID for quick access and faster booking.
Add a venue to the user's favorites by venue_id.
| Name | Required | Description | Default |
|---|---|---|---|
| venue_id | Yes |
Changes observed during successful MCP inspections.
v1.0.0Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"v0.5.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the core mutation (add favorite) but does not mention authentication requirements, duplicate handling, what happens if venue_id is invalid, or whether the operation is reversible.
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?
One short sentence with no filler; the action, target, and parameter are all front-loaded. Every word earns its place.
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 one-parameter mutation with no annotations and no output schema, the description covers the essential call surface. It is less complete on behavioral edge cases (duplicates, errors, response shape), so an agent has the minimum but not full context.
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 0%, but the description explicitly assigns meaning to the parameter: venue_id identifies the venue to add. It goes beyond the raw schema's integer constraint, though it doesn't say how to obtain a valid venue_id (e.g., via search_venues).
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 ('Add'), names the exact resource ('a venue to the user's favorites'), and identifies the key input ('by venue_id'). This clearly differentiates it from sibling tools like resy_list_favorites and resy_remove_favorite without needing to inspect schemas.
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 intended use is implied: call this when the user wants to favorite a venue. However, the description gives no explicit guidance on when not to use it (e.g., when the venue is already favorited, or when to prefer list_favorites/remove_favorite).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.