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

Annotations already declare readOnlyHint=true and openWorldHint=true, indicating safe, read-only behavior. The description adds value by specifying the returned fields ('star count, description, language, and URL'), which is beyond what the annotations provide. No contradictions.

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 two sentences, front-loading the purpose and then detailing output. Every sentence contributes value without repetition or fluff.

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 output schema, the description adequately summarizes return values. It covers the tool's purpose, parameters, and output format. Though additional details like pagination or sorting could be included, the current description is sufficient for a search tool with two parameters.

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 100%, so baseline is 3. The description adds meaningful context for both parameters: query is explained with examples, and max_results is described with default and upper limit. This goes beyond bare schema definitions.

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 verb 'Search', the resource 'GitHub repositories', and the purpose 'discover code, projects, and libraries'. It distinguishes itself from sibling tools like get_github_repo by focusing on search rather than retrieving a single repository.

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 provides specific use cases: 'finding libraries, examples, or competitive projects in specific domains'. While it doesn't explicitly state when not to use it, the context is clear enough for an AI agent to decide when to invoke this tool over siblings.

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