Propose architecture

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

With no annotations, the description carries the full burden, and it delivers: it discloses that suggestions are rule-based (not LLM-generated), that finalize triggers a state-changing store operation, and that behavior depends on prior state ('finalized scope' and 'human's experience level'). It does not detail whether storing overwrites an existing stack, but the dual-mode read/write behavior is well covered.

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 sentences with no filler: the core purpose is front-loaded in sentence one, and the two conditional behaviors fill out the rest. Every sentence adds distinct value, and the conditional structure mirrors the tool's actual branching logic.

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?

For a dual-mode tool with no annotations and no output schema, the description covers a lot: return content of the suggestion mode, the write behavior of finalize mode, and the prerequisites (finalized scope, experience level). The main omissions are the precise semantics of the store operation (overwrite vs append) and the role of session_id, both of which an agent could reasonably infer but are not stated.

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%, so the description must compensate. It explains finalize's semantic effect (store vs suggest) and enumerates the data object's contents ('frontend/backend/database/hosting + rationale'), mapping cleanly onto the schema's properties. session_id is only implied via the session context, leaving a minor gap.

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 states a clear verb-resource pair ('Draft or finalize the tech stack') and then specifies the exact deliverable: frontend/backend/database/hosting + rationale. The two modes (suggest vs store) are both named, making the tool's scope unambiguous. It stops short of explicitly naming sibling tools like generate_database_schema, so differentiation is contextual rather than direct.

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 internal branch selection is explicit and useful: omit finalize to get a suggestion, pass finalize: true with data to store. This gives the agent clear invocation logic. However, there is no guidance on when to choose this tool over conceptually adjacent siblings (evaluate_project_scope, generate_database_schema, generate_roadmap), so cross-tool routing is left to inference.

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