prefetch_related

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

No annotations provided, so the description carries full burden. It discloses the use of static analysis on imports, callees, test files, and learned patterns, but does not describe side effects, idempotency, or state changes from pre-loading. Adequate but not fully transparent.

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: one sentence for purpose, one for methods, then an Args list. It is front-loaded and every sentence adds value. No wasted words.

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 lacking annotations, the description explains purpose and parameters well. It does not mention output format, but an output schema exists to cover that. It could mention prerequisites or limitations, but overall it is fairly complete.

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 description adds substantial meaning to each parameter: file_path is 'the file currently being accessed', source_content is 'the source code content (for static analysis)', language is 'Programming language (python, typescript, rust)'. This goes far beyond the bare schema, which has low coverage.

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: predict and pre-load context needed next. It explains the methods (static analysis + learned patterns). However, it does not explicitly differentiate from sibling tools like recall_relevant or smart_read, which also deal with context retrieval, so it's not a 5.

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 gives context on when to use (when you want to pre-load likely needed context) but does not specify when not to use or mention alternatives. It lacks explicit guidelines for selection among siblings.

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