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ntopology-mcp

ntopology-mcp

by sohumsuthar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

validate_graph

Validate nTop notebooks to identify dangling inputs, duplicate IDs, empty literals, and incorrect list wiring, ensuring they load and build correctly.

Instructions

Check a notebook for problems that stop it loading or building: dangling inputs, duplicate ids, literals with no value, and list parameters wired with several inline edges instead of a core.list block.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "notebook": "<notebook>"
    },
    "name": "validate_graph"
  }
}

Input Schema

NameRequiredDescriptionDefault
notebookYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral burden. It says 'check', implying non-destructive validation, and lists specific checks. This adds context beyond the name. The only gap is that it doesn't describe the result format (return value, errors) or any side effects, but it is otherwise transparent.

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 single, information-packed sentence that front-loads the purpose ('Check a notebook for problems that stop it loading or building') and then lists specific cases. Every phrase adds value; there is no fluff.

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 description lists the problem categories it detects, which is helpful, but it lacks information about the expected output (does it return a list, a boolean, or throw an exception?) and the exact nature of the notebook parameter. Given there is no output schema and low parameter coverage, these gaps leave the tool only partially complete.

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

Parameters2/5

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. The single parameter 'notebook' is only described as part of the phrase 'Check a notebook', which fails to clarify whether it's a path, ID, content, or something else. The description adds no semantic detail beyond what the schema (a string) already provides.

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 the verb ('check') and resource ('a notebook'), and enumerates specific problem types (dangling inputs, duplicate ids, etc.). This distinguishes it from sibling tools like run_notebook (executes) and inspect_notebook (presumably examines), making the purpose unambiguous.

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 conveys when to use this tool: when checking for specific loading/building problems. It implies a pre-run validation role. However, it doesn't explicitly name alternatives or state when not to use it, so it stops short of full guidance.

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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