find_retailersGlobal cross-node retailer (demand) search. Filter by region / node / parent_banner.
| Name | Required | Description | Default |
|---|---|---|---|
| node | No | ||
| limit | No | ||
| region | No | ||
| parent_banner | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not state whether the operation is read-only, whether it has side effects, pagination behavior, or any performance constraints. The description merely restates the search purpose and filter options without revealing operational traits.
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 core purpose and lists the filter dimensions immediately. There is no fluff or redundancy; 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?
While the output schema exists to define return values, the description is thin for a tool with four parameters and zero annotation or schema-level parameter docs. It does not explain filter semantics (exact match? wildcard?), the limit parameter, or how the tool behaves with nulls. It is acceptable for a simple search but leaves gaps.
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 has no property descriptions (0% coverage), so the description must compensate. It names three filter parameters (region, node, parent_banner) but omits limit entirely and gives no detail about value formats, matching semantics, or optionality. This is insufficient for an agent to construct correct calls without additional inference.
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 states the tool performs a 'retailer (demand) search' across a 'global cross-node' scope. This clearly identifies the resource (retailers) and the operation (search), and the parenthetical '(demand)' distinguishes it from maker/producer searches. It is specific and avoids tautology.
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?
No guidance is given on when to use this tool versus sibling tools like find_makers or check_eligibility. The description simply says what it does, leaving the agent to infer applicability. There are no exclusions, alternatives, or context hints.
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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Multiple tools overlap heavily: resolve_gtin, resolve_sparks, and get_signal_chain all resolve GTINs to different but closely related signals, and resolve_node vs node_market both return the same node with makers/retailers counts. Agents will struggle to pick the correct tool without reading deep into descriptions.
Most tools follow a clear verb_noun pattern (check_, count_, find_, get_, list_, resolve_), but node_market breaks the pattern and the get_ vs resolve_ distinction is not obvious. Overall readable with only minor deviations.
14 tools is well-scoped for a CPG knowledge graph covering GTIN resolution, eligibility, supply/demand search, node metadata, taxonomy, and brand coverage. Each tool reasonably earns its place without overwhelming the surface.
The surface covers the apparent domain comprehensively: GTIN resolution, eligibility checks, maker/retailer discovery, node/spine queries, SKU taxonomy, brand listings, signal chains, and registries. For a read-only knowledge graph, there are no obvious dead ends or missing core operations.