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list_workflows

Discover the semantic workflows in a codebase by auto-clustering functions into 25 domains. Get per-workflow narrative impact, member count, risk metrics, and runtime hypotheses without relying on code structure.

Instructions

Auto-clusters the entire repository graph into 25 semantic workflow domains (Authentication, Authorization, Session, Payments, Billing, Checkout, Cart, Notifications, Webhooks, Realtime, Queue, Caching, Persistence, Sync, Search, Onboarding, Profile, Admin, Analytics, AI, Routing, Orchestration, Reporting, Configuration, Infrastructure) using a weighted vote across path tokens, import tokens, and symbol tokens. Returns per-workflow narrative impact, member count, max risk, average risk, and runtime risk hypotheses. This is the workflow-first view of the repository — the most important Veris tool for understanding what a codebase actually does rather than how it's structured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The WorkflowClassifier.report() method orchestrates classification by calling classify() then aggregate(), producing the final WorkflowReport (workflows, aggregates, unassigned nodes) consumed by handleListWorkflows().
    public report(
        report: RepositoryIntelligenceReport,
        graph: BehavioralGraph,
        diff: DiffReport,
        risks: RiskReport[]
    ): WorkflowReport {
        const domains = this.classify(report, graph);
        const aggregates = this.aggregate(domains, diff, risks);
        const uncategorized = domains.find(d => d.kind === WorkflowKind.Uncategorized);
        const nodeById = new Map(graph.getNodes().map(n => [n.id, n]));
        const unassigned: GraphNode[] = uncategorized
            ? uncategorized.memberNodeIds.map(id => nodeById.get(id)).filter((n): n is GraphNode => !!n)
            : [];
        return { workflows: domains, aggregates, unassigned };
    }
Behavior5/5

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

Given no annotations, the description fully covers behavior: it details the clustering mechanism ('weighted vote across path tokens, import tokens, and symbol tokens') and exactly what is returned (narrative impact, member count, risk metrics). No contradictions.

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, well-structured sentence that front-loads the main action. It includes some detail (list of domains) which adds value without being overly verbose.

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?

For a tool with no parameters and no output schema, the description provides ample context: it explains the clustering approach, the output contents, and its significance. It is complete enough for an agent to select and invoke correctly.

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?

There are zero parameters, so the schema carries no burden. According to guidelines, 0 params yields a baseline of 4. The description does not need to add parameter semantics, but it clearly explains what the tool does without them.

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 uses specific verbs ('Auto-clusters', 'Returns') and clearly identifies the resource ('repository graph', 'workflow domains'). It distinguishes itself from siblings by stating it's the 'most important Veris tool for understanding what a codebase actually *does*', making the purpose distinct.

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 implies when to use (for workflow-first understanding) but does not explicitly state when not to use or provide direct alternatives. However, the context is clear enough that an agent can infer its primary use case.

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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