software_get_robot_softwareSoftware bill of materials for one robot: OS, middleware, AI runtime, motion control, fleet management. Requires an access right on the robot requested; without it the call returns a refusal naming that robot.
| Name | Required | Description | Default |
|---|---|---|---|
| robot_id | Yes | ||
| blocks_only | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation safe/read-only/idempotent. The description adds useful behavioral context beyond annotations: the caller needs an access right on the specific robot, and unauthorized calls return a refusal naming that robot.
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?
Two sentences: the first front-loads the core purpose and contents, the second adds a critical access/error condition. Every sentence earns its place with no redundancy.
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 description covers the BOM contents and auth behavior, but with no output schema and an undefined blocks_only parameter, there are notable gaps. It is minimally viable but not fully 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 description coverage is 0%, so the description must explain parameters. It only loosely implies robot_id via 'one robot' and says nothing about blocks_only, leaving the boolean parameter's semantics unclear.
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 identifies the tool as a software bill of materials scoped to one robot and enumerates the component layers (OS, middleware, AI runtime, motion control, fleet management). This clearly differentiates it from list/compare/get-robot siblings.
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 tool's scope is explicitly limited to one robot, and the access-right requirement plus refusal behavior gives a clear precondition. It does not explicitly name alternatives, so it stops short of a 5.
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.
The layer prefixes and action verbs make most tools clearly distinct, but the three list_robots variants return the same catalogue and the three get_robot variants share identical commercial fields, creating real selection ambiguity. The descriptive warnings like 'call one, not three' help but do not fully resolve the overlap.
All 27 tools follow a uniform <layer>_<action>_<object> snake_case pattern with consistent verbs: get, list, compare. There are no mixed naming conventions or stylistic deviations.
27 tools is above the comfortable range and is inflated by triplication: list_robots, get_robot, compare_robots, list_signals, and system_status are repeated across layers with only minor differences. A layer parameter could consolidate a large portion of the surface without losing functionality.
As a read-only intelligence platform, the set covers catalogue listing, comparison, robot profiles, BOM details, block analysis, dependency analysis, signals, and system health across hardware, mechanical, and software layers. The main gap is the lack of a consolidated multi-layer robot view, though agents can compose one from the three get_robot variants.