github_recommend_stack

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

With no annotations, the description carries the full behavioral disclosure burden. It does indicate advisory behavior ('recommends', 'copy-paste installation commands') and implies read-only operation, but it does not explicitly state that it does not modify state, install anything, require special auth, or behave unexpectedly when no starred tools match.

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 a single, readable sentence with the key purpose front-loaded. The opening phrase 'AI Agent Tech-Stack Recommender' is slightly redundant with 'recommends', but the overall structure is focused and free of filler.

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?

For a recommendation tool with only one required parameter, the core input and output are reasonably clear. However, with no output schema and no annotations, the agent is left guessing at return format, default behavior, and edge cases such as no recommendations or language restrictions. The description covers the essentials but not the full call context.

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%, so the schema already documents all three parameters. The description ties 'project brief or task description' to task_description and mentions commands/rationale, but adds little semantic value beyond the schema for language or max_recommendations.

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 a specific verb ('recommends') and resource ('vetted starred tools and libraries'), plus the triggering context ('project brief or task description'). This distinguishes it from siblings like list_starred or search_stars, which are about retrieving data rather than synthesizing recommendations.

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 when to use it—when a project brief or task description exists—but provides no exclusions, prerequisites, or explicit alternatives. It does not reference any sibling tool or explain when one of the many starred-repo tools would be more appropriate.

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