Analyzes client code patterns to infer API schemas and generate stubs. Use consumer source directory and tool name to create producer stubs with optional handler.
Generate API stubs from client usage patterns. Infers schema from how client code calls the API.
| Name | Required | Description | Default |
|---|---|---|---|
| consumerDir | Yes | Path to client source directory | |
| toolName | Yes | Name of the endpoint/tool to scaffold | |
| includeHandler | No | Include handler stub |
Changes observed during successful MCP inspections.
Input schema / properties / consumerDir / descriptionPrevious value: -"Path to consumer source directory"New value: +"Path to client source directory"Input schema / properties / toolName / descriptionPrevious value: -"Name of the tool to scaffold producer for"New value: +"Name of the endpoint/tool to scaffold"Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose all behavioral traits. It only says 'infers schema' but doesn't mention whether it writes files, requires permissions, or has side effects. Critical behavior like overwriting existing stubs is not disclosed.
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?
Two sentences with no redundancy. Every word is functional and front-loaded.
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?
No output schema or annotations exist. The description does not mention output format (e.g., file paths, generated code language) or prerequisites. For a code generation tool, this is insufficient for an agent to use 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?
The input schema covers 100% of parameters with descriptions. The description adds context that the stubs are generated from client usage patterns, but does not elaborate on how consumerDir influences inference or how includeHandler affects output.
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 clearly states the tool generates API stubs and infers schema from client usage. However, it does not explicitly differentiate from its sibling scaffold_consumer, which likely does the opposite (generate client stubs from API 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?
No guidance is provided on when to use this tool versus alternatives like scaffold_consumer or other code generation tools. Context like prerequisites (e.g., consumerDir must exist) is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.