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generate_devcontainer

Create devcontainer.json configuration files using natural language prompts to define development environments for VS Code, Cursor, or MCP-compatible editors.

Instructions

Generate devcontainer.json from natural language description

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesNatural language description of the development environment
workspaceRootNoWorkspace root path.
baseTemplateNoOptional template to start from
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 states the tool generates a file but doesn't disclose whether it overwrites existing files, requires specific permissions, has rate limits, or what the output looks like. For a tool that likely creates or modifies configuration files, this lack of detail is a significant gap.

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 a single, efficient sentence that directly states the tool's function. It is front-loaded with the core purpose and has no unnecessary words, making it highly concise and well-structured.

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

Completeness2/5

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

Given the complexity of generating configuration files and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'devcontainer.json' is, how the generation works, error handling, or return values. For a tool with no structured output and behavioral unknowns, more context is needed.

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 three parameters. The description adds no additional meaning beyond what the schema provides, such as examples of natural language prompts or how the baseTemplate interacts with generation. Baseline 3 is appropriate when the schema does the heavy lifting.

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: 'Generate devcontainer.json from natural language description'. It specifies the verb ('generate') and resource ('devcontainer.json'), and distinguishes it from siblings like 'modify_devcontainer' or 'build_devcontainer'. However, it doesn't explicitly differentiate from 'list_templates' or 'test_devcontainer' in terms of scope.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, when to choose this over 'modify_devcontainer' or 'build_devcontainer', or any exclusions. The agent must infer usage from the name and context alone.

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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