Learning Status

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does add value by stating the exact output shape ('same dict as learning-loop-mcp status --json') and content (metrics + validation findings), but it does not explicitly state whether the operation is read-only, what errors may occur, or any other side-effect information.

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 two sentences with no filler. It front-loads the core purpose, adds a precise CLI-equivalence detail, and avoids repeating schema 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?

The tool is simple and has an output schema, so return-value documentation is covered. However, with no annotations and no parameter explanations, the description leaves gaps around parameter semantics and any behavioral caveats, making it minimally adequate but not 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%, and the description provides no explanation of the parameters. The parameter names (project, tests_dir, errors_path, lessons_path) are somewhat self-explanatory, but with low coverage the description should compensate, and it does not.

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 identifies the tool as returning the learning-loop status of a project, including metrics and validation findings. It is not a tautology because it adds specifics about content and references the equivalent CLI command, though it does not explicitly differentiate among sibling tools.

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 intended use is implied: agents should call this when they need project learning-loop status or validation metrics. However, there is no explicit guidance about when to prefer this over siblings like learning_context or solutions, and no exclusions or alternatives are mentioned.

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