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Understand-Anything MCP Server

Understand-Anything MCP Server

by VIethoangnguyenle
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_layer_info

List architectural layers or fetch nodes within a specific layer to inspect codebase organization.

Instructions

List architectural layers, or get nodes belonging to a specific layer.

Args: layer_name: Layer name to inspect. Leave empty to list all layers. project: Project name.

Returns: Layer listing or nodes within a specific layer.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_layer_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
projectNo
layer_nameNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A3.9/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It explains the effect of layer_name (empty vs specified) and the return type (layer listing or nodes), but does not disclose behavior when project is empty or mention prerequisites, leaving some ambiguity.

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 a compact docstring with clear Args and Returns sections, no redundant prose, and the primary purpose is front-loaded. Every line contributes meaningfully.

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

Completeness3/5

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

The tool has only two optional parameters and an output schema exists, but the description misses important edge-case behavior: what happens if project is left empty, and how project and layer_name interact (e.g., listing layers for a specific project). These gaps prevent full completeness.

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 description coverage is 0%, so the description must compensate. layer_name is well described with a conditional behavior ('Leave empty to list all layers'), but project is only described as 'Project name,' which adds little beyond the schema title and does not clarify optionality or interaction with layer_name.

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 states 'List architectural layers, or get nodes belonging to a specific layer,' providing a specific verb and resource. This distinguishes it from sibling tools like query_nodes and get_graph_metadata by focusing on layer-level operations.

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 provides clear context on when to use the tool: listing all layers when layer_name is empty, or inspecting nodes in a specific layer. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.

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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