Get architecture

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

No annotations provided, so description carries full burden. It discloses the read-only nature ('Get'), explains the algorithm (Leiden community detection), and optional scoping, but does not explicitly state no side effects 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.

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

The description is concise, front-loaded with the main purpose, and each sentence adds value without redundancy. It uses clear structure and formatting.

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?

Given the tool's complexity and presence of an output schema, the description covers key aspects: purpose, scope options, and the unique cluster analysis. It is sufficient for an AI agent to understand the tool's capabilities.

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?

Schema coverage is 67%, so baseline is 3. The description adds context for the 'path' parameter (scoping to directory prefix) and explains the 'clusters' aspect, but does not elaborate on all aspect values beyond what the schema already provides.

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 clearly states it provides a high-level architecture overview including packages, services, dependencies, and clusters via community detection, distinguishing it from sibling tools that focus on code snippets or graph queries.

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?

Implicitly guides when to use (for architecture overview) but lacks explicit when-not or alternatives. The description gives a clear context for usage, but could be improved by comparing with siblings.

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