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verify_data_flow

Renders a component route and matches real network traffic against static analysis predictions. Detects unexpected API calls and confirms mutations by driving user interactions.

Instructions

Render a component's route and check the REAL network traffic against the endpoints that static analysis (get_data_flow, including child components and stores) predicts. Matching is method-aware. The key output is unexpectedApiCalls — observed calls that map to NO predicted endpoint, i.e. real source-vs-runtime drift (dynamic URLs, app-level fetches, or genuine divergence). verdict is 'confirmed' when every observed call is accounted for. Pass actions to drive interactions (fill/click/...) before the network is read — that's how predicted MUTATION endpoints (POST/PUT/DELETE) get exercised and verified. Requires the dev server to be running.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fileNoOptional path substring to disambiguate when the name matches multiple files
nameYesComponent or composable name whose data flow to verify (e.g., "PatientDetail").
depthNoHow deep to trace the child component tree for predictions (default 3).
paramsNoValues for dynamic route segments, e.g. {"id": "123"}.
actionsNoOptional interactions to run after the route loads and BEFORE network traffic is evaluated (e.g. fill a form and click Save to exercise a POST).
settleMsNoExtra milliseconds to wait before reading the observed network traffic (default 500).
Behavior4/5

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

Without annotations, the description carries full burden. It outlines the workflow (render, interact, wait, compare) and explains key outputs (unexpectedApiCalls, verdict) and behavior (method-aware, delayed reading). Minor omission of side effects, but overall transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph but efficiently conveys all necessary information. It could be improved with structural elements like bullet points, but it remains readable and focused.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (6 params, nested objects, no output schema), the description thoroughly covers the workflow, expected outputs, and prerequisites (dev server). It leaves no critical gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the purpose of actions (exercising mutations) and settleMs (wait time), and provides examples for within. This elevates it above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool's function: comparing real network traffic against static analysis predictions. It uses a specific verb (verify), resource (data flow), and methodology (method-aware matching), differentiating it from siblings like get_data_flow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains when to use the tool (after static analysis) and notes a prerequisite (dev server running). It also details how actions drive mutation endpoint verification, providing sufficient context despite lacking explicit alternatives.

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

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