trace_data_flow

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

With no annotations provided, the description carries the full burden. It discloses that the tool identifies transformations, side effects, and data origins/destinations, and that it returns 'Comprehensive data flow analysis.' However, it does not disclose whether it is read-only, performance implications, scope limitations, or how the tracing is performed (e.g., static analysis via AST). This leaves gaps in behavioral transparency.

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 a clear purpose statement, bullet points for usage, and distinct sections for Args and Returns. It is appropriately sized and front-loaded. The 'Perfect for debugging' sentence is somewhat redundant given the preceding bullets, but not distractingly so.

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 (6 parameters, no output schema, no annotations), the description provides a solid foundation: purpose, usage guidance, parameter explanations, and a Returns summary. However, it omits practical details like whether a git repository is required, performance considerations, or the exact structure of the returned analysis, which would help an agent invoke it more confidently.

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 0%, so the description must compensate. The Args section provides descriptions for all six parameters, adding meaning beyond the schema's bare titles and types. For example, data_identifier gives examples and max_depth clarifies its purpose, though some descriptions remain vague (e.g., 'how deep' lacks units, working_directory vs repo_path distinction is subtle).

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 opens with a specific verb+resource: 'Trace how specific data flows through the system from source to destination.' It also lists concrete examples of data identifiers (user_id, email, order_data) and clearly differentiates from sibling tools like trace_dependency_impact by focusing on the flow of a specific data element rather than dependency impact.

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 explicit when-to-use conditions via 'Use this tool when you need to understand:' followed by four concrete scenarios. However, it does not include when-not-to-use conditions or mention alternative sibling tools, which prevents a perfect score.

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