list_learned_instrumentsLists profiles saved from previous instrument learning calls, helping you identify known SCPI devices and reuse their configurations on this machine.
Profiles saved from previous learn_instrument calls (this machine).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=false, so the safety profile is already known. The description adds useful context that the data comes from previous learn_instrument calls and is machine-local, but it doesn't discuss freshness, persistence, or return shape.
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 fragment with no wasted words. It efficiently communicates the data source and scope for a simple listing tool.
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 zero-parameter, read-only listing tool with an output schema and annotations, the description gives enough context about what data will be returned. The main gap is the lack of routing guidance relative to list_instruments, but the core purpose is clear.
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 tool takes zero parameters, and schema description coverage is 100%, so there are no parameter semantics to clarify. Per the rubric, zero-parameter tools receive a baseline of 4 when no param information is needed.
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 names the resource precisely: profiles saved from previous learn_instrument calls on this machine. It implicitly differentiates from general instrument lists by scoping to learned profiles, but does not explicitly contrast with the sibling list_instruments tool.
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?
It gives no explicit when-to-use guidance, no prerequisites, and no alternatives. The origin from learn_instrument suggests a context, but the agent must infer that this tool should be called to review previously learned profiles.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.