Skip to main content
Glama

design_architecture

Create a cloud architecture from a natural-language description. Specify provider, region, budget, and compliance to get components, connections, tier assignments, and cost estimate.

Instructions

Design a cloud architecture from a natural-language description.

Primary entry point for greenfield architecture design. Returns a complete ArchSpec (YAML-serializable dict) with components, connections, tier assignments, and a cost estimate.

When to use: You have a requirement (prose) and need a concrete architecture with services, wiring, and cost. Use modify_architecture to iterate on an existing spec, or chat_create_session + chat_send for multi-turn refinement.

Behavior: Calls an LLM provider (Anthropic or OpenAI depending on configured keys) — incurs API costs per invocation. Deterministic post-processing layers (cost engine, catalog lookup) apply safe defaults like encryption-at-rest, multi-AZ on databases, and auto-scaling. Does not deploy or modify any cloud resources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionYesPlain-English description of the system to design. Include workload type (e.g. 'HIPAA-compliant 3-tier healthcare API'), traffic expectations, and any stack preferences. The LLM uses this to select services, tiers, and connections.
providerNoTarget cloud provider for the generated architecture. Values: 'aws', 'gcp', 'azure', 'databricks'. Default 'aws'.aws
regionNoCloud region for the generated architecture (e.g. 'us-east-1' for AWS, 'us-central1' for GCP, 'eastus' for Azure). Used to set region-aware pricing and compliance constraints (e.g. FedRAMP requires US regions).us-east-1
budget_monthlyNoOptional monthly budget cap in USD. When set, the architect biases toward instance tiers and managed services that fit under this cap.
complianceNoOptional list of compliance frameworks the architecture must satisfy. Values from: 'hipaa', 'pci-dss', 'soc2', 'fedramp', 'gdpr'. Influences service selection (e.g. BAA-eligible services for HIPAA, FIPS-compliant services for FedRAMP) and encryption defaults.
Behavior5/5

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

Describes internal behavior: calls an LLM provider incurring costs, deterministic post-processing applies safe defaults, and explicitly states it does not deploy or modify resources. With no annotations provided, this fully covers transparency.

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?

Description is concise and well-structured: front-loaded with purpose, then output, usage, and behavior. Every sentence adds value without redundancy.

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?

Covers purpose, usage, behavior, and return format despite no output schema. Mentions output is an ArchSpec with components, connections, etc. Minor omissions like error handling or rate limits keep it from a 5.

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% with detailed parameter descriptions including examples and defaults. The tool description adds overall context but does not significantly extend per-parameter meaning beyond the schema, hence baseline score of 3.

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's purpose: 'Design a cloud architecture from a natural-language description.' It identifies itself as the primary entry point for greenfield architecture design, and distinguishes from siblings like modify_architecture and chat tools.

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?

Explicit 'When to use' section provides clear guidance: use when you have a requirement and need a concrete architecture. It also specifies alternatives such as modify_architecture for iteration and chat sessions for multi-turn refinement.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/xmpuspus/cloudwright'

If you have feedback or need assistance with the MCP directory API, please join our Discord server