fetch_github_readme

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (fetches README content), mentions a default value for max_bytes (20KB), and specifies the return format (JSON with content, size, metadata). However, it doesn't address potential errors (e.g., missing README, rate limits, authentication needs) or pagination behavior, leaving some behavioral aspects unclear.

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 well-structured with clear sections (purpose, usage guidelines, parameters, returns, example) and every sentence earns its place. It's front-loaded with the core purpose and usage context, avoiding redundancy while maintaining completeness. The example at the end reinforces understanding without unnecessary elaboration.

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 moderate complexity (2 parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, parameters, returns, and provides an example. However, without an output schema, it could benefit from more detail on the JSON structure (e.g., specific metadata fields) or error handling, leaving minor gaps in 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?

The schema description coverage is 0%, so the description must fully compensate. It provides detailed parameter semantics: 'repo' is explained with format ('owner/repo') and an example ('psf/requests'), and 'max_bytes' is explained with its purpose ('Maximum content size') and default value ('default 20KB'). This adds significant meaning beyond the bare schema.

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 specific action ('Fetch README file') and resource ('from a GitHub repository'), distinguishing it from siblings like get_file_content() for arbitrary files or get_repo_tree() for directory structure. It provides a concrete example ('psf/requests') that reinforces the purpose.

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 explicitly includes 'USE THIS WHEN' and 'BEST FOR' sections that specify when to use this tool (for project overview, quick start, basic documentation) and when not to use it (for deeper code exploration). It names two alternative tools (get_repo_tree(), get_file_content()) for different use cases, providing clear guidance on tool selection.

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