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datasets_doordash_stores_facets

Get facet counts for DoorDash store data by country, state, city, tags, status, price range, or DashPass eligibility, scoped by filters, to discover marketplace tags.

Instructions

Facet stored DoorDash stores. Returns terms aggregation counts for the DoorDash store directory. Facet enum: country, state, city, tags, display_status, price_range, dash_pass_eligible. Accepts the same filter parameters as search to scope the aggregation. Use facet=tags to discover the marketplace tag values accepted by the tag filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text search over store name, address, city, and marketplace tags
tagNoSingle marketplace tag filter
cityNoExact city filter
facetYesFacet enum: country, state, city, tags, display_status, price_range, dash_pass_eligible
stateNoState/region code filter
countryNoISO-3166-1 alpha-2 country filter
dash_pass_onlyNoKeep only DashPass-eligible stores

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.9

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It discloses that the call is an aggregation ('returns terms aggregation counts') and that filters scope it, but says nothing about ordering, bucket limits, whether zero-count terms are returned, or that it is a read-only operation. Adequate but incomplete.

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?

Four short sentences, front-loaded with the purpose followed by the return shape, then the enum, then the practical tip. No filler and no restated boilerplate.

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?

There is no output schema, so the description is the only source for the return shape, and it only says 'terms aggregation counts' without describing the key/value bucket structure. For a 7-parameter aggregation tool with no annotations, one more sentence on the response shape would close the gap.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description restates the facet enum (already in schema) and adds the useful semantic link between 'facet=tags' and the 'tag' filter parameter, but adds nothing about how the other filter params (q, city, state, country, dash_pass_only) interact with the facet choice.

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?

States a specific verb and resource ('Facet stored DoorDash stores') and clarifies the return type as 'terms aggregation counts for the DoorDash store directory,' which distinguishes it from a plain search. It does not name the sibling tools (search/item/nearby) explicitly, but the aggregation framing is a strong differentiator.

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

Usage Guidelines4/5

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

Gives an explicit use case — 'Use facet=tags to discover the marketplace tag values accepted by the tag filter' — which is a concrete discovery workflow. It also notes it accepts the same filter parameters as search to scope the aggregation. It lacks explicit when-not-to-use guidance versus the search sibling.

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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