get_ai_at_work_courseReturns the public outline of AI at Work, the employee AI training course: modules, what is included, seat tiers and the guarantee. Use it for questions about employee training. It does not return transcripts or quiz content. To check readiness, use assess_ai_readiness.
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Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false and non-destructive, so the safety profile is covered. The description adds genuine scope boundaries by naming excluded content (transcripts, quiz content), which helps an agent avoid a wrong tool call, though it says nothing about freshness or whether the outline is subject to change.
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?
Three short sentences, front-loaded with what is returned, followed by the usage condition and the exclusion/alternative. Every sentence carries distinct routing information with no 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?
No output schema exists, yet the description enumerates the returned fields (modules, inclusions, seat tiers, guarantee), so an agent knows what to expect. With no parameters and annotations covering the safety profile, nothing needed to call this correctly is missing.
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, so per the rubric the baseline is 4. There is no parameter syntax for the description to explain, and it correctly does not invent any.
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?
States a specific verb and resource ('Returns the public outline of AI at Work') and enumerates exactly what the payload contains: modules, inclusions, seat tiers, guarantee. This is clearly distinguishable from siblings like get_pricing or get_faq, which cover adjacent but different content.
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?
Explicitly names the use case ('questions about employee training'), states what it will not serve ('does not return transcripts or quiz content'), and routes the agent to the correct alternative ('To check readiness, use assess_ai_readiness'). When-to-use, when-not-to-use, and alternative are all present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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