get_learning_context

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

No annotations are provided, so the description carries full burden. It describes the output but fails to disclose behavioral traits such as authentication requirements, rate limits, or side effects. For a read-oriented tool, the transparency is lacking.

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 sentence that front-loads the action and lists key output components. It is concise with no filler, though the list format could be slightly clearer if broken into bullet points. Still, it earns its place.

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?

With no output schema, the description should fully explain return values. It lists high-level components but does not detail their structure or types. Adequate for simple use, but agents need more specifics to parse results reliably.

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 only parameter ('days') is fully documented in the input schema with description, min, and max. The tool description adds no additional meaning beyond the schema, meeting the baseline expectation for 100% schema coverage.

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 uses a specific verb ('Get') and resource ('aggregated learning progress') and lists concrete components (error patterns, language activity, skill tree progress, streaks, personalized recommendations), making the tool's purpose immediately clear and distinct from siblings like get_git_context or scan_project.

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?

No explicit guidance on when to use this tool versus alternatives (e.g., generate_daily_summary). The context is implied by the 'aggregated learning progress' focus, but there are no exclusion criteria or examples of appropriate use cases.

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