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write_architecture_doc

Create architecture decision records or system design documents, index them into a vector store, and push to git automatically. Supports Mermaid diagrams for component relationships.

Instructions

Create an architecture decision or system design document, index it, and auto-push.

    Side effects: creates architecture/YYYY-MM-DD-{slug}.md in the docs path,
    indexes it into the vector store, and pushes to git if configured.
    Overwrites an existing file with the same title.

    Use for system design, ADRs, and component relationships.
    Use write_api_doc() for HTTP endpoint specs, write_best_practice()
    for coding patterns, write_bugfix_summary() after fixing bugs.
    Include a Mermaid diagram in the diagrams field for best results.

    Args:
        title: Short title (e.g. "Payment Service Architecture")
        overview: High-level description of the system/component
        decisions: Key architectural decisions made and why
        trade_offs: Alternatives considered and rejected, pros/cons
        components: Optional component breakdown (optional)
        diagrams: Optional Mermaid or ASCII diagrams (optional)
        project: Target project name (optional)

    Returns:
        Saved filename, chunk count, and whether auto-push succeeded.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYes
overviewYes
decisionsYes
trade_offsYes
componentsNo
diagramsNo
projectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses side effects: creates file with specific path pattern, indexes into vector store, pushes to git, and overwrites existing files. This is thorough, though it lacks details on authorization or rate limits.

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

Conciseness4/5

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

The description is moderately long but well-structured with sections for side effects, usage guidelines, and parameters. It is efficient, though a slight trim could improve conciseness.

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?

Given the tool's 7 parameters, 4 required, and output schema with return fields, the description comprehensively covers purpose, side effects, usage, parameter meanings, and return value. It leaves no significant gaps.

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

Parameters5/5

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

Schema description coverage is 0%, but the description provides meaningful explanations for each parameter in the Args section, including optional ones like components, diagrams, and project. This adds value beyond the schema.

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 clearly states the tool creates an architecture decision or system design document, indexes it, and auto-pushes. It distinguishes from siblings by naming alternative tools like write_api_doc, write_best_practice, and write_bugfix_summary.

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?

The description explicitly states when to use the tool (for system design, ADRs, component relationships) and when not to (use other tools for specific purposes). It provides clear alternatives.

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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