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

Annotations already indicate readOnlyHint=true. The description adds context about auto-routing of inputs and that presentation models are display scaffolding, which are behavioral nuances beyond annotations. No contradictions.

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?

Four sentences, front-loaded with the main purpose, no filler. Every sentence adds essential information. Highly efficient.

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?

Given no output schema, the description adequately explains that outputs are computed results from all models and notes presentation scaffolding. Could be more explicit about output format, but overall sufficient for the tool's complexity.

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 covers 100% of parameters with descriptions. The description adds value by explaining 'Inputs are auto-routed to the correct model by property name' and reinforcing that omitted inputs use defaults. This supplements the schema.

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 starts with 'Run a computation on a MetaModel project,' clearly stating the verb and resource. It adds specifics about auto-routing inputs and distinguishing presentation models from engineering outputs, making the purpose distinct from sibling tools (get_schema, list_projects).

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?

Explicitly advises using metamodel_get_schema first to discover inputs/outputs, providing a clear prerequisite. However, it lacks explicit 'when not to use' guidance, but given the sibling set, the usage context is clear.

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