Generate SQLAlchemy models

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

Annotations declare readOnlyHint=true and openWorldHint=false; the description goes well beyond this by disclosing the return shape (rendered models.py as a single string), a known functional limitation (composite FKs unsupported), and an input constraint (identifier regex). This is exactly the behavioral context annotations cannot carry.

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?

Dense and front-loaded: output format and coverage lead, then gaps and identifier policy. Slightly long, but nearly every clause carries actionable information rather than padding.

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 an output schema present, the description needn't detail return values, yet it still states the output artifact. Combined with gap disclosure, identifier rules, and alternative routing, an agent has everything needed to decide and invoke correctly.

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 50%; the nested `database` param is documented in the schema (secondary read-only DB, list_databases). The description adds meaning for the primary `schema` param by constraining valid identifiers, but doesn't spell out the database-selection semantics itself.

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?

States a specific verb (read + emit), the resource (PostgreSQL schema -> SQLAlchemy 2.0 declarative models), and the exact output artifact (DeclarativeBase + Mapped[T] + mapped_column). It enumerates covered features, cleanly distinguishing it from siblings like generate_prisma_schema, generate_drizzle_schema, and generate_diesel_schema.

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?

Gives explicit when-not guidance: composite FKs are a documented v1 gap, and objects with delimited identifiers must go to export_table/dump_database instead. This routes the agent to alternatives on the exact edge cases that would otherwise cause a wrong call.

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