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generate-code-stub

Create code stubs for functions, classes, or methods in various languages based on detailed descriptions and optional file context.

Instructions

Generates a code stub (function, class, etc.) in a specified language based on a description. Can optionally use content from a file (relative path) as context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
classPropertiesNoFor classes: list of properties with names, optional types, and descriptions.
contextFilePathNoOptional relative path to a file whose content should be used as additional context.
descriptionYesDetailed description of what the stub should do, including its purpose, parameters, return values, or properties.
languageYesThe programming language for the stub (e.g., 'typescript', 'python', 'javascript')
methodsNoFor classes/interfaces: list of method signatures with names and descriptions.
nameYesThe name of the function, class, interface, etc.
parametersNoFor functions/methods: list of parameters with names, optional types, and descriptions.
returnTypeNoFor functions/methods: the expected return type string.
stubTypeYesThe type of code structure to generate (function, class, etc.)
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the core action ('Generates') and optional file context, but lacks details on permissions, rate limits, error handling, or what the generated output looks like (e.g., format, completeness). For a tool with 9 parameters and no annotations, this is a significant gap in transparency.

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 front-loaded and efficient: a single sentence that states the core purpose and key optional feature. Every word earns its place, with no redundancy or unnecessary elaboration, making it easy for an AI agent to parse quickly.

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

Completeness3/5

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

Given the tool's complexity (9 parameters, no output schema, no annotations), the description is incomplete. It covers the basic purpose but lacks details on behavioral traits, output format, or error scenarios. However, the high schema coverage (100%) mitigates some gaps, making it minimally adequate but with clear room for improvement.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 9 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'language' and 'description' as key inputs and hinting at 'contextFilePath' as optional file context. It doesn't provide additional syntax, examples, or constraints beyond what's in the schema descriptions.

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 the tool's purpose: 'Generates a code stub (function, class, etc.) in a specified language based on a description.' It specifies the verb ('Generates'), resource ('code stub'), and key parameters (language, description). However, it doesn't explicitly differentiate from siblings like 'generate-fullstack-starter-kit' or 'refactor-code', which might also involve code generation.

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 context by mentioning 'based on a description' and 'optionally use content from a file as context,' but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'generate-fullstack-starter-kit' (which might be for larger projects) or 'refactor-code' (which modifies existing code). No exclusions or clear alternatives are stated.

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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