get_dataflow

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

The description adds 'Read-only' which aligns with annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true). It also describes the output structure, including the return JSON format, which goes beyond annotations.

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 (three sentences) and front-loaded with the core purpose. Every sentence adds value, including the return JSON summary and alternative tool mention.

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 4 parameters, high schema coverage, and no output schema, the description adequately covers the input and output, including a JSON summary, making it complete for this analysis tool.

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 100%, so the baseline is 3. The description adds context by explaining that parameters are tracked for flows and mutations, enhancing understanding beyond the schema definitions.

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 defines the tool as performing intra-function dataflow analysis, tracking parameter flows, mutations, and return paths. It distinguishes itself from siblings by mentioning taint_analysis, making its purpose specific and differentiated.

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 states when to use ('to understand data transformations within a function') and when not to ('for security-focused data flow use taint_analysis instead'), providing clear usage context.

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