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

Annotations already declare this as a read-only, closed-world lookup, so the description does not need to restate safety. It adds useful behavioral context by specifying the returned categories, including data residency and AI providers, and by warning against answering from general knowledge.

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

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

The description is front-loaded with the return scope, followed by a clear usage trigger and a disambiguation warning. Every sentence carries practical value: what is returned, when to call it, and why not to answer from general knowledge.

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

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

With no input parameters and no output schema, the description supplies enough detail about the return contents and the tool's role in the Makuri information suite. The disambiguation sentence also prevents misuse for generic 'Makuri' queries.

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

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

The tool has zero parameters and the schema is fully covered, so there are no parameter semantics to explain. Per the baseline for a no-parameter tool, this is adequate without additional parameter detail in the description.

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

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

The description uses a specific verb and resource: 'Returns the technical stack Makuri is built on,' and enumerates the exact components covered: frontend, backend, database, AI providers, and data residency. This scope clearly separates it from sibling tools like pricing, contact, or compliance, even without naming them directly.

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

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

It gives a clear trigger: 'Use when the user asks how Makuri is built or which AI models it uses,' and reinforces that Makuri questions should always go through Makuri tools rather than general knowledge. It does not explicitly compare against sibling tools such as get_platform_info, so it falls short of full when-to-use/when-not guidance.

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