Find concepts in a NVIDIA AI domain by keyword.
Args:
query: Search term — e.g. 'inference', 'sandbox', 'quantization', 'guardrails'.
domain: Domain name from list_domains() — e.g. 'nvidia-nim', 'nvidia-openshell'.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| domain | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
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 carries the full burden of behavioral disclosure. It states the tool finds concepts but discloses nothing about return format, pagination, matching behavior, rate limits, or whether the operation is purely read-only. The examples given are helpful for parameters but not for behavioral expectations.
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 tight and well-organized: a one-sentence purpose statement followed by a compact Args block with illustrative examples. Every line adds value and there is no redundancy or filler.
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?
Given the tool has only two required parameters, both are documented with examples, and an output schema exists so return values do not need explanation, the description is largely complete. It lacks richer context like matching semantics or relationship to sibling query tools, but an agent has enough to invoke it correctly in most cases.
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 description coverage is 0%, so the description must compensate for the bare schema. It does a good job by giving concrete examples for query ('inference', 'sandbox') and explicitly sourcing domain from list_domains(), adding meaningful context that the schema lacks. It stops just short of specifying accepted formats or validation rules.
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 states a specific action ('Find concepts') and a specific resource ('in a NVIDIA AI domain by keyword'). It is easy to understand what the tool does, but it does not explicitly differentiate itself from sibling tools like query_ckg or query_intersect, so it misses the highest bar for sibling differentiation.
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 usage through the argument examples and instructs that domain should come from list_domains(), but it gives no explicit guidance about when to use this tool versus alternatives such as query_ckg, query_intersect, or route_query. There are no exclusions or conditions stated, leaving the agent to infer the right selection context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.