resolve_nodeResolve one node: its GS1 license + its makers (supply) + its retailers (demand).
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes |
| 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?
With no annotations, the description carries the burden of behavioral disclosure. It does reveal the return scope (license, makers, retailers), but it does not state whether this operation is read-only, whether it performs external lookups, or what happens for unknown nodes. It is not misleading, but it is thin.
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 tight sentence that leads with the action and resource, then breaks the result into three parenthetical components. Every word adds value and there is no redundant restatement of the tool 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?
The output schema likely covers return values, but the definition leaves usage context and parameter semantics under-specified. Given the large sibling list and no annotations, a short note on when to use this resolver versus the more specific find_* tools would make it contextually complete.
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 0%, and the description does not clarify what 'code' means, what format it should take, or how it relates to a node beyond the weak inference from 'one node.' With a single required parameter, even a short format hint or example would add meaning; none is provided.
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 ('resolve') and specifies exactly what is included: GS1 license, makers (supply), and retailers (demand). This clearly distinguishes it from sibling tools like find_makers or find_retailers, which each cover only part of this scope.
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 description implies this is the combined tool for resolving a single node's license, supply, and demand, but it never explicitly says when to choose it over find_makers/find_retailers or what to do if you only need one aspect. The 'one node' qualifier is helpful but is not a full routing statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.