get_signal_chainReturn the full ACM-68000 signal chain for a GTIN (zero-pads to 14).
| Name | Required | Description | Default |
|---|---|---|---|
| gtin | 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 full behavioral burden. It does add useful input-normalization behavior ('zero-pads to 14') and implies a read-only operation via 'Return', but it does not disclose error behavior, permission requirements, or side effects. The presence of an output schema reduces the need to describe return structure.
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, front-loaded sentence with no wasted words. The parenthetical adds a functionally important normalization detail without bloating the text.
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 lookup with an output schema, the description is minimally adequate. It covers what the tool returns and how the input is normalized, but it leaves out usage context, the meaning of the opaque domain term 'ACM-68000 signal chain', and any mention of when it should be preferred over sibling tools.
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%, so the description must compensate. It does identify the only parameter, gtin, and adds the zero-padding behavior, which clarifies the expected format. It still does not specify valid GTIN lengths or whether any additional formatting is expected beyond padding.
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 clearly identifies the operation ('Return') and the resource ('full ACM-68000 signal chain for a GTIN'), which distinguishes it from siblings like get_kernel or resolve_gtin. However, it assumes domain knowledge of what an 'ACM-68000 signal chain' is, so it is not fully self-explanatory.
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 about when to use this tool versus siblings such as resolve_gtin, get_kernel, or count_gtin_coverage. The agent must infer usage from the tool name and the one-line description, with no exclusions or alternative routing.
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.