Skip to main content
Glama

it_ops_recommend_architecture

Generates IT architecture recommendations using your objective and optional structured inputs through a domain agent.

Instructions

Run the it_ops domain agent action recommend_architecture.

Routes through the platform's domain-agent dispatcher under your JWT, tenant, and company scope.

Args: message: Free-text objective for the action. inputs: Optional JSON string of structured inputs for the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageNo
inputsNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must convey behavioral traits. It only mentions routing through the dispatcher under specific scopes, but it does not disclose whether the action reads or modifies resources, has side effects, or requires special permissions. For a recommendation tool, the lack of read-only status is a gap.

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 concise (two sentences plus args list) and front-loaded with the action name. However, it is under-specified, lacking detail that would justify its brevity. It could be more efficient by including key behavioral or usage info without adding length.

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

Completeness2/5

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

An output schema exists, so return values need not be described. However, the description omits what kind of architecture is recommended, expected input context, examples of objectives, or how results are presented. This leaves the agent underinformed for a complex domain action.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It describes 'message' as a free-text objective and 'inputs' as an optional JSON string, which adds basic meaning. However, it does not explain valid formats for inputs, expected structure, or constraints, leaving significant ambiguity for an agent.

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

Purpose3/5

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

The description states it runs the 'recommend_architecture' action, indicating it provides architecture recommendations. However, it lacks specificity on what the recommendation entails (e.g., cloud architecture, software architecture) and does not distinguish it from sibling it_ops tools like 'it_ops_analysis_review' or 'it_ops_design_review'.

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

Usage Guidelines2/5

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

No guidance on when or why to use this tool versus alternatives. It does not clarify prerequisites, ideal use cases, or scenarios where another tool would be more appropriate. The mention of JWT/tenant/company scope is authorization context, not usage guidance.

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/RPasquale/lightbulb-mcp'

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