generate_project_guidelines

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

No annotations are provided, so the description carries the full burden. It discloses key behaviors: uses web search for latest official docs, style guides, best practices; uses Vertex AI model (gemini-2.5-pro-exp-03-25) with Google Search; generates actionable rules and recommendations. However, it lacks details on rate limits, authentication needs, output format specifics (e.g., Markdown structure), or potential errors. For a tool with no annotations, this is a moderate level of transparency.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by details on method and requirements. Every sentence adds value (e.g., explaining the use of web search and AI model). It could be slightly more concise by combining some clauses, but it avoids redundancy and waste.

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 no annotations, no output schema, and a single parameter with full schema coverage, the description is moderately complete. It covers the purpose, method, and input requirement adequately. However, for a tool that performs web search and AI synthesis, it lacks details on output format, error handling, or performance considerations, which could be important for an agent to use it correctly.

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 description coverage is 100%, with the parameter 'tech_stack' well-described in the schema as an array of strings for technologies and versions. The description adds minimal semantics beyond the schema, only reiterating that it's required and specifying the input as 'a specified list of technologies and versions.' This meets the baseline of 3 since the schema does the heavy lifting.

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 the tool's purpose: 'Generates a structured project guidelines document based on a specified list of technologies and versions.' It specifies the output format (Markdown), the input (tech stack), and the method (web search for official docs, style guides, best practices, synthesis via Vertex AI). This distinguishes it from all sibling tools, which are mostly about querying, file operations, or saving outputs, not generating guidelines from tech stacks.

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?

The description implies usage context: when you need project guidelines for a given tech stack, using web search and AI synthesis. It explicitly states 'Requires tech_stack' as a prerequisite. However, it does not specify when not to use it (e.g., vs. manual research or other tools) or name alternatives among siblings, though the sibling list includes no direct alternatives for guideline generation.

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