memorybank_generate_project_docs

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

No annotations are provided, so the description carries full burden. It discloses the AI model used and that projectId is mandatory, but does not explain side effects (e.g., overwriting, file creation location), idempotency, or any destructive potential, leaving behavioral understanding incomplete.

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 front-loaded with the core action and lists documents, but includes extraneous sentences (e.g., 'Útil para que agentes menos avanzados...') that could be streamlined. It is moderately concise but not optimally structured.

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 clarify what the tool returns or how docs are delivered. It lists the documents but omits return format, file location, or output behavior, leaving a significant gap for agent 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?

Input schema covers 100% of parameter descriptions. The description adds emphasis on projectId being mandatory and matching the indexing ID, but provides no new semantic details beyond the schema. Baseline 3 is appropriate.

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 generates structured project documentation using AI, listing the six specific markdown documents. It also mentions complementing semantic search, which provides some differentiation from sibling tools like memorybank_search, but does not explicitly name alternatives.

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 implies use for obtaining global project context ('complementa la búsqueda semántica precisa') and for less advanced agents, but lacks explicit when-to-use vs when-not-to-use guidance or direct comparison to siblings like memorybank_get_project_docs.

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