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doordash_search

Search DoorDash restaurants by name, cuisine, or food type to find available dining options for delivery or pickup.

Instructions

Search for restaurants on DoorDash by name, cuisine, or food type

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (e.g. 'pizza', 'thai food', 'McDonalds')

Implementation Reference

  • The handler logic for 'doordash_search' which uses the 'api.search.searchStores' function to query and return a list of restaurants.
    server.registerTool(
      "doordash_search",
      {
        description:
          "Search for restaurants on DoorDash by name, cuisine, or food type",
        inputSchema: {
          query: z
            .string()
            .describe("Search query (e.g. 'pizza', 'thai food', 'McDonalds')"),
        },
      },
      ({ query }) =>
        wrap(async () => {
          const stores = await api.search.searchStores(query);
          if (stores.length === 0)
            return ok(`No restaurants found for "${query}".`);
    
          const text = stores
            .map(
              (s, i) =>
                `${i + 1}. **${s.name}** — ${s.cuisine} (${s.distance}) [store ID: ${s.storeId}]`,
            )
            .join("\n");
          return ok(`Search results for "${query}":\n\n${text}`);
        }),
    );
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full disclosure burden. It fails to indicate whether authentication is required, whether the operation is read-only, what the return format contains (restaurant objects, IDs, etc.), or any rate limiting. Only the basic search intent is disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence of nine words with zero redundancy. Every word earns its place by conveying the action, platform, resource, and search dimensions.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter search tool without output schema, the description is minimally adequate. However, given the complex DoorDash ecosystem (evident from 20+ sibling tools), it lacks prerequisite context (e.g., whether address/login must be set first) or hints about the return structure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description adds value by categorizing the query parameter's purpose into three semantic buckets (name, cuisine, or food type), which helps the agent understand valid query patterns beyond the schema's generic examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Search' and resource 'restaurants on DoorDash', and specifies searchable attributes (name, cuisine, food type). It implicitly distinguishes from the sibling 'doordash_convenience_search' by specifying 'restaurants', though it doesn't explicitly name the sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by specifying the tool searches for 'restaurants' (suggesting use when seeking food establishments) and lists valid query types (name, cuisine, food type). However, it lacks explicit guidance on when NOT to use it or direct comparison to doordash_convenience_search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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