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get_architecture_recommendation

Get architectural guidance before implementing major features, with recommended design patterns, file structure, technology choices, and implementation steps.

Instructions

RECOMMENDED: Get expert architectural guidance before implementing major features.

This tool provides flexible project lookup. You can specify any combination of identifiers, and it will resolve to the matching project.

Priority: project_id > workspace_path > project_name

WHEN TO USE:

  • Before implementing significant new features or capabilities

  • When you're unsure about the best architectural approach

  • For complex features that affect multiple parts of the system

  • When choosing between different implementation strategies

  • To ensure consistency with existing architecture

  • Before making structural changes to the codebase

HIGHLY RECOMMENDED FOR:

  • New major features

  • Significant refactoring efforts

  • Integration with external systems

  • Changes to core architecture

  • Performance-critical implementations

THIS PROVIDES:

  • Recommended approach and design patterns

  • File structure and organization suggestions

  • Technology and library recommendations

  • Implementation steps and considerations

  • Rationale for the recommendations

AFTER RECEIVING RECOMMENDATIONS:

  1. Review the suggested approach

  2. Save the decision with save_decision() if you adopt the recommendation

  3. Implement following the suggested structure

  4. Update architecture tracking with update_architecture()

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoAdditional context - requirements, constraints, preferences (optional)
project_idNoProject ID from create_project() (optional if project_name or workspace_path provided)
constraintsNoList of constraints - e.g., ["must use PostgreSQL", "must be stateless"] (optional)
project_nameNoProject name to look up (alternative to project_id)
workspace_pathNoWorkspace directory path (alternative to project_id)
feature_descriptionYesClear description of what you're building (required) - e.g., "User authentication system with JWT tokens"
implementation_styleNoPreferred approach - "modular", "monolithic", or "auto" (default: "modular")modular
Behavior4/5

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

No annotations are present, so the description carries the full burden. It discloses project lookup priority (project_id > workspace_path > project_name), the nature of the output (recommendations, design patterns, file structure, etc.), and next steps after receiving recommendations. This goes beyond the schema and provides meaningful behavioral context.

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

Conciseness3/5

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

The description is front-loaded with the key purpose but is quite lengthy, including redundant 'WHEN TO USE' and 'HIGHLY RECOMMENDED FOR' sections. While organized in clear sections, it could be condensed without losing meaning.

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?

With no output schema and no annotations, the description compensates by listing what the tool provides (recommended approach, design patterns, implementation steps) and giving post-usage steps. It covers the essential context for using the tool, though it omits potential error scenarios.

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

Parameters4/5

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

Schema coverage is 100% with all 7 parameters described. The description adds the identifier priority order and the role of feature_description, which clarifies how parameters should be used together, exceeding the baseline.

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 'Get expert architectural guidance before implementing major features' with a specific verb and resource. However, it doesn't explicitly differentiate from sibling tools like analyze_architecture or get_design_patterns, so it falls short of full differentiation.

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

Usage Guidelines4/5

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

The 'WHEN TO USE' and 'HIGHLY RECOMMENDED FOR' sections provide explicit contexts such as before major features, when unsure about approach, and for complex features. It lacks explicit when-not-to-use or named alternatives, but the guidance is clear and actionable.

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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