lookup

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

With no annotations, the description provides adequate behavioral info: pattern matching, fallback, typical token length. However, it does not explicitly state read-only nature or side effects, though lookup is inherently read-only.

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 and front-loaded with purpose. The list of patterns aids understanding, though it could be more structured (e.g., bullet points). No wasted sentences.

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 (codebase lookup) and presence of an output schema, the description covers key behavior (patterns, fallback, token length). It lacks details on error cases or handling of no matches, but is largely 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?

Schema description coverage is 0%, so description carries the burden. It explains 'question' (natural language question) and 'output_format' (text/json), but does not explain 'repo_path' or 'exclude_dirs'. This partial coverage meets baseline but does not fully compensate.

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: 'Answer a specific question about the codebase.' It provides concrete examples of understand patterns (e.g., 'where is X defined?', 'what calls X?'), distinguishing it from sibling tools like `search_semantic` or `symbols`.

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 clear context on when to use the tool: for specific questions with known patterns. It mentions fallback to fuzzy symbol search, implying alternatives, but does not explicitly state when not to use it or list sibling tools.

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