suggest_learning_projects

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It outlines the tool's process steps (read code, analyze patterns, generate ideas, return links), which adds useful context beyond basic functionality. However, it lacks details on potential limitations, error handling, or performance aspects like rate limits or authentication needs, leaving some behavioral traits unclear.

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 appropriately sized and front-loaded, starting with usage triggers and then detailing the tool's steps. Each sentence serves a purpose: the first sets context, the second lists triggers, and the third explains the process. However, it could be slightly more concise by integrating the trigger list more smoothly, but overall it's efficient with minimal waste.

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 (3 parameters, no output schema, no annotations), the description is moderately complete. It explains the tool's purpose, usage, and process, but lacks details on output format (beyond 'formatted Marble platform links'), error cases, or dependencies. Without annotations or output schema, more behavioral context would improve completeness, but it's adequate for basic understanding.

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 schema description coverage is 100%, so the input schema already documents all parameters (topic, codeContext, difficulty) with descriptions and enums. The description doesn't add any additional meaning or examples beyond what the schema provides, such as clarifying how parameters interact or affect output. Thus, it meets the baseline but doesn't enhance parameter understanding.

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's purpose: to generate project ideas for learning by analyzing code patterns and technologies. It specifies the verb 'instructs the AI agent to' with steps like 'Read relevant code', 'Analyze patterns', and 'Generate project ideas'. However, it doesn't explicitly differentiate from sibling tools like generate_marble_link or generate_slides_link, which might serve different purposes.

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 explicit usage guidelines, stating 'Use this tool when the user wants to PRACTICE or BUILD something to learn' and listing specific trigger phrases such as 'projects', 'practice', 'build something', etc. This gives clear context for when to invoke the tool, though it doesn't mention when not to use it or alternatives, but the trigger list is comprehensive enough for effective selection.

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