tzero-mcp

generate_architecture_blueprint

Generate a system architecture blueprint with Mermaid diagrams, layer boundaries, and module hierarchies for onboarding or RFC review. Returns Markdown for ARCHITECTURE.md documentation.

Instructions

Generates a complete system architectural specification (ARCHITECTURE.md) including auto-generated Mermaid component topology diagrams, layer boundaries, and module hierarchies.

Behavior:

Read-only by default: Returns formatted markdown text. Does NOT write files unless invoked via CLI export.
Idempotent: Produces deterministic AST topology when use_ai=False.
Cost: 100% free and local by default (use_ai=False). If use_ai=True, uses configured environment API key (e.g. NVIDIA NIM / OpenAI) for enriched architectural narrative.

Usage Guidelines:

When to use: Create or update system architecture documentation and visual Mermaid flow diagrams for onboarding or RFC review.
Sibling Routing: For checking rule compliance against defined boundaries, use 'enforce_architecture_boundaries'. For basic dependency trees, use 'query_module_dependencies'.

Returns: Comprehensive Markdown document with live Mermaid diagrams, layer classifications, and component interfaces.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No	Optional model identifier override (e.g. 'deepseek-v4.1-flash', 'gpt-4o') when use_ai is True.
`use_ai`	No	When True, uses configured 3rd-party AI or Free AI Cloud to synthesize deep architecture analysis; when False (default), runs 100% offline AST analysis.
`project_root`	No	Root directory of the repository to analyze and document (default: current directory).	.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv0.1.1Oct 16, 2026

addedInput schema / properties / model / description

Added value: +"Optional model identifier override (e.g. 'deepseek-v4.1-flash', 'gpt-4o') when use_ai is True."

addedInput schema / properties / project_root / description

Added value: +"Root directory of the repository to analyze and document (default: current directory)."

addedInput schema / properties / use_ai / description

Added value: +"When True, uses configured 3rd-party AI or Free AI Cloud to synthesize deep architecture analysis; when False (default), runs 100% offline AST analysis."

First observedv0.1.0Oct 15, 2026

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so well: read-only by default (returns markdown, does not write files unless via CLI export), idempotent/deterministic when use_ai=False, and cost/auth implications (free and local by default; uses configured API key when use_ai=True).

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?

Front-loaded with the purpose and organized into clear Behavior/Usage/Returns sections with bullets, so it scans well. Slightly verbose and mildly repetitive (the free/local vs AI cost point is restated in Behavior and Returns), keeping it just below a 5.

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?

For a zero-required-parameter generation tool with an output schema present, the description is complete: it covers scope, safety/write behavior, cost/auth tradeoffs, sibling routing, and even summarizes returns. Nothing needed to invoke it correctly is missing.

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, making 3 the baseline. The description reinforces use_ai's cost/network implications and model's role, adding marginal value but no syntax or format detail 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?

States a specific verb+resource: generates a complete system architectural specification (ARCHITECTURE.md) with Mermaid diagrams, layer boundaries, and module hierarchies. This is distinguishable from siblings like generate_repo_map or query_module_dependencies without opening any schema.

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?

Explicitly gives when-to-use (create/update architecture docs and diagrams for onboarding or RFC review) and provides Sibling Routing that names two alternatives with the condition selecting each ('enforce_architecture_boundaries' for rule compliance, 'query_module_dependencies' for dependency trees).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.