Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must fully disclose behavior. It reveals that the tool uses ctx.sample() to invoke other MCPs and returns an LLM summary string, which is helpful. However, it does not explain side effects, error handling, or what occurs when called (e.g., does it actually execute the orchestration?). This is adequate but not comprehensive.

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 concise, using a single paragraph with clear sections: purpose, mechanism, args, returns. Every sentence adds value, with no unnecessary repetition or fluff.

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's low complexity (1 parameter) and the presence of an output schema (not described but acknowledged), the description is fairly complete. It explains the orchestration behavior and provides an example. Minor gap: it doesn't specify what triggers the orchestration or any prerequisites.

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 input schema has 0% description coverage, so the description must compensate. It describes the 'goal' parameter as 'Natural language' with one example ('Summarize Bumi specs...'). This adds some context but lacks constraints (e.g., length, format, scope). The compensation is weak.

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 states it handles high-level goals like specs, SDK setup, virtual twin, and fleet composition, and that it orchestrates other tools via ctx.sample(). This gives a clear purpose but does not differentiate from its sibling 'bumi_tool', leaving ambiguity about when to use each.

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 mentions it uses ctx.sample() to call sibling tools (bumi_tool, resonite, robotics), implying it is for orchestration rather than direct execution. However, it lacks explicit when-to-use or when-not-to-use guidance and does not describe alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses behavioral traits such as the conditional nature of robot_status (requires BUMI_ROBOT_URL for ping) and mentions that hardware control requires a documented local API or ROS bridge. It also notes that market info is not legal advice. However, it does not explicitly state idempotency or side effects, limiting a higher score.

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 structured with clear sections and bullet points for operations. Each sentence serves a purpose, but it is slightly verbose in parts (e.g., the portmanteau rationale could be tightened). Overall, it is well-organized and front-loaded with key information.

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's complexity (multiple sub-operations, conditional behavior), the description covers most aspects: operation details, prerequisites (BUMI_ROBOT_URL), and a return structure note. An output schema exists, so the brief return description is acceptable. Missing details on error handling or response format, but overall 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?

The single parameter 'operation' has no enum values in the schema (0% coverage). The description adds significant meaning by listing seven specific operation values (info, specs, sdk_links, etc.) with detailed explanations for each. This fully compensates for the schema gap and provides rich semantic context.

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 the tool is for retrieving information about the Bumi humanoid robot, including hero product info, OSS links, and a fleet virtual-twin map. It distinguishes from the sibling tool 'bumi_agentic_workflow' by being a single entry for discovery and setup, establishing a unique purpose.

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 provides detailed context for each sub-operation (info, specs, sdk_links, etc.) but does not explicitly state when to use this tool over the sibling or when not to use it. The portmanteau rationale offers some implicit guidance, but without clear exclusions or alternatives, the score is adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.