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clearskies-py

clearskies MCP Server

generate_model

Define a clearskies Model class with specified columns, backend, and optional hooks. Generates ready-to-use model code for your application.

Instructions

Generate a clearskies Model class definition.

Args:
    name: The model class name in PascalCase (e.g. "User", "OrderProduct").
    columns: List of column definitions. Each dict has:
        - name (str): Column name
        - type (str): Column type (e.g. "String", "Integer", "Uuid", "BelongsToId")
        - options (dict, optional): Keyword arguments for the column constructor
    backend_type: Backend class name (default: "MemoryBackend").
    backend_options: Keyword arguments for the backend constructor.
    id_column_name: The name of the id column (default: "id").
    hooks: Optional list of hook methods to include stubs for (e.g. ["pre_save", "post_save", "save_finished"]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
hooksNo
columnsYes
backend_typeNoMemoryBackend
id_column_nameNoid
backend_optionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It does not mention whether the tool writes files, returns code, requires an existing project, or has side effects. The description only states the generation intent without explaining observable behavior, which is a significant gap for a code generation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear purpose sentence followed by a detailed Args list. Every sentence adds value, and the length is proportional to the tool's 6 parameters and the nested column structure. It is front-loaded and efficiently organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the presence of an output schema, the description covers input parameters thoroughly and states the purpose clearly. However, it lacks behavioral context such as output format or constraints on column types, and it does not reference related sibling tools like get_column_info for additional details. Still, for the provided information, it is reasonably complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description fully compensates by providing rich details for each parameter. It explains the PascalCase requirement for name, the structure of columns with name/type/options, defaults for backend_type and id_column_name, and examples for hooks. This adds significant meaning beyond the schema's bare type definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it generates a clearskies Model class definition with a specific verb and resource. However, it does not explicitly distinguish itself from sibling tools like generate_model_with_relationships, which also generates a model. The name is specific enough, but the description lacks explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through its detailed parameter list, but there is no explicit guidance on when to use this tool versus alternatives like generate_model_with_relationships or scaffold_project. No exclusions or alternative recommendations are provided, so guidance is only implied.

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

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