NetworkX MCP Server

centrality_measures

Calculate degree, betweenness, closeness, and eigenvector centrality for graphs to identify node importance and analyze network structure.

Instructions

Calculate multiple centrality measures (degree, betweenness, closeness, eigenvector)

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": {
      "graph": "<graph>"
    },
    "name": "centrality_measures"
  }
}

Input Schema

Name	Required	Description	Default
`graph`	Yes
`measures`	No	List of measures: degree, betweenness, closeness, eigenvector

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Aug 5, 2026

TDQS

B3.3/5.0

Behavior2/5

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 only lists measure names and provides no information about return format, the required 'graph' parameter semantics, default behavior when 'measures' is omitted, or any side effects or prerequisites.

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?

A single sentence with no filler. It is front-loaded with the verb 'Calculate' and directly specifies the resource and supported measures. Every word earns its place.

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

Completeness2/5

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

Given no annotations, no output schema, and an underdocumented graph parameter, the description is too sparse. It does not explain the return value, the nature of the graph input, or behavior when 'measures' is absent, which would be critical for a tool with this complexity.

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 50%; 'measures' has a description but 'graph' does not. The description adds no additional meaning for the graph parameter and only repeats the allowed measure values already present in the schema.

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 tool calculates multiple centrality measures and enumerates them (degree, betweenness, closeness, eigenvector). This verb+resource+scope effectively distinguishes it from sibling tools like degree_centrality and betweenness_centrality.

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 plural 'multiple' and the list of measures imply this tool is for computing several centralities at once, but there is no explicit statement of when to use it versus individual centrality tools or any alternatives/exclusions. Usage context is inferable but not directly guided.

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