find_brandFuzzy-match an artist or brand name against Dropify's tracked brands to find its page, store count, and free store option.
Fuzzy-match an artist or brand name against Dropify's tracked brands. Returns the brand name, its Dropify page, how many stores are tracked, and which store (if any) is free.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Artist or brand name to look up, e.g. 'Taylor Swift' or 'bts' |
Changes observed during successful MCP inspections.
v0.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the full behavioral burden. It does disclose the return contents (brand name, page, tracked store count, free store), which is useful, but omits fuzzy-match behavior such as multiple matches, ranking, no-match results, and whether it is strictly read-only.
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?
Two sentences with zero padding. The action is front-loaded and the return-shape sentence follows immediately, with no redundant restatement of the name.
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 read lookup with no output schema, the description covers what it does and what comes back. The remaining gap is edge-case behavior on ambiguous or empty matches, which would matter for an agent deciding how to follow up.
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 coverage is 100% for the single query parameter, so the baseline is 3. The description adds the notion that matching is fuzzy and that the input may be an artist or brand name, but only marginally beyond the schema's own 'Artist or brand name to look up' text.
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?
States a specific verb and resource: fuzzy-matching an artist or brand name against Dropify's tracked brands. It also enumerates the return fields, so an agent knows exactly what it gets back. Siblings (official_stores, restock_alert_link) serve unrelated roles, so no explicit differentiation is needed.
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?
Usage is implied by the name and the lookup framing, but there is no guidance on when to call it versus the sibling tools, or on prerequisites. Nothing about what to do when nothing matches, or whether it should be run before restock_alert_link.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.