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generate_project

Generate complete project scaffolds under architectures like Clean, Onion, or Microservices. Returns structured, saveable file content without writing files directly.

Instructions

Generate a full project scaffold under a named architecture (Clean Architecture, Onion, Vertical Slice, Modular Monolith, Microservices, CQRS, Repository+UoW). Recommended for small-to-medium scaffolds in one call; for large projects call generate_files repeatedly per module instead of expecting one call to build an entire system. Returns structured, saveable file content — never writes to disk itself.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specYesDescription of the project/system to scaffold
modelNoModel to use (default: deepseek-reasoner)
databaseNoDatabase technology (e.g. PostgreSQL, SQL Server)
languageNoTarget programming language
providerNoAI Provider
frameworkNoFramework in use (e.g. ASP.NET Core, NestJS)
architectureNoArchitecture/template to use (e.g. Clean Architecture, Onion, Vertical Slice, Modular Monolith, Microservices, CQRS, Repository+UoW)
target_folderNoRoot folder for the scaffolded project
project_contextNoExisting project structure/conventions the scaffold must fit into, if any
naming_conventionNoNaming convention to follow
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: 'Returns structured, saveable file content — never writes to disk itself,' which tells the agent that the tool is non-destructive and requires the caller to persist results. It does not detail edge cases like existing projects or error responses, but the main behavioral contract is clear.

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?

Three sentences, each earning its place: first states purpose and scope, second gives usage vs. alternative guidance, third explains return behavior and side-effect. Information is front-loaded and free of filler.

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

Completeness5/5

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

Despite having 10 parameters, no output schema, and no annotations, the description covers the essential context: what the tool produces (full scaffold), when to use a different tool (large projects), the output format (structured, saveable file content), and its non-destructive nature. This is sufficient for an agent to select and invoke the tool correctly.

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 coverage is 100%, so the baseline is 3 and the description does not need to explain each parameter. The description adds little beyond repeating architecture examples already present in the schema's architecture field. It does not enrich semantics for spec, model, database, or other parameters.

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

Purpose5/5

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

The description uses a specific verb and resource: 'Generate a full project scaffold under a named architecture' and lists concrete architecture options. This clearly distinguishes it from sibling tools like generate_files or generate_code, which focus on individual files or code snippets.

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

Usage Guidelines5/5

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

It explicitly states when to use this tool ('Recommended for small-to-medium scaffolds in one call') and when not to ('for large projects call generate_files repeatedly per module instead of expecting one call to build an entire system'). It also clarifies the output behavior ('never writes to disk itself'), giving strong decision context.

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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