search_code

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

The description discloses automatic indexing and re-indexing of changed files, which is a key behavioral trait. No annotations are provided, so the description handles the transparency burden well.

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, behavioral note, args, and return. While slightly verbose in examples, every sentence adds value and it is easily scannable.

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 complexity of semantic search with auto-indexing, the description covers purpose, usage context, parameter details, behavior, and return format. It lacks a warning about potential indexing delays but is otherwise 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 input schema has 0% description coverage, but the description explains each parameter in natural language: query as 'Natural language description,' project_path as 'Absolute path,' limit with default and maximum. This adds significant meaning beyond the 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 tool performs semantic code search, distinguishes from text matching, and provides concrete examples. It differentiates from sibling tools (index_codebase, index_status) which are about indexing.

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 advises when to use: 'when you want to find code related to a concept without knowing exact variable/function names.' It implicitly contrasts with text search but does not explicitly mention alternatives like grep. However, the guidance is sufficient.

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