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UrbanDiver

Local DeepWiki MCP Server

by UrbanDiver

get_layer_dependencies

Read-onlyIdempotent

Detect upward dependency violations in Python codebases by analyzing layer imports. Returns categorized file counts and dependency edges.

Instructions

Analyze architectural layer dependencies in a Python codebase. Categorizes files into layers (web, handlers, services, generators, core, providers, models) and detects upward dependency violations where lower layers import from higher layers. Returns layer file counts, dependency edges, and violations.

No prior indexing required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
repo_pathYesPath to the repository to analyze
summary_onlyNoReturn only violation count without full layer details (default: false)
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds value by stating 'No prior indexing required,' which is a useful behavioral trait beyond annotations. It does not contradict any annotations.

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

Conciseness5/5

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

The description is concise, with three sentences that front-load the key purpose and details. Every sentence adds value, and there is no redundant or unnecessary content.

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 moderate complexity and 100% schema coverage, the description adequately covers what the tool does, its return values (file counts, edges, violations), and the key prerequisite (no indexing needed). No output schema exists, but the description compensates well.

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

Parameters3/5

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 about what the tool returns (file counts, edges, violations) but does not elaborate on parameter choices beyond the schema. Thus, it meets but does not exceed expectations.

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 specifies the tool's purpose: analyzing architectural layer dependencies, categorizing files into layers, and detecting upward dependency violations. It uses specific verbs and resources, and the focus on layers distinguishes it from siblings like analyze_architecture or get_cross_module_dependencies.

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

Usage Guidelines3/5

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

The description states the tool analyzes layer dependencies and mentions no prior indexing is required, which provides context. However, it does not explicitly compare itself to alternatives or specify when not to use this tool, leaving some ambiguity for the agent.

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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