NetworkX MCP Server

analyze_author_impact

Calculate author impact metrics such as h-index from a citation graph. Input an author name and graph to retrieve their impact measures.

Instructions

Analyze author impact metrics including h-index

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>",
      "author_name": "<author_name>"
    },
    "name": "analyze_author_impact"
  }
}

Input Schema

Name	Required	Description	Default
`graph`	Yes
`author_name`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.1.0Aug 5, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of disclosing behavioral traits. It does not state that the operation is read-only, whether it has side effects, or what happens if the graph or author is missing. The description is silent on permissions and output behavior.

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 concise sentence that is front-loaded with the primary action. It contains no filler or redundant information.

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?

The description is too minimal for a tool with no annotations, no output schema, and 0% schema coverage. It does not explain what impact metrics are included, what the output format is, or what kind of graph is expected. This is inadequate for an agent to reliably invoke the tool.

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 0%, so the description must illuminate the parameters. 'Analyze author impact metrics' implies author_name refers to the researcher and graph refers to the underlying dataset, but it does not explain what the graph should represent or the expected values. Some semantic value is added over bare schemas, but it's insufficient.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Analyze author impact metrics including h-index' clearly states the tool's purpose with a specific verb and resource. It distinguishes itself from sibling graph analysis tools by focusing on author-specific bibliometric metrics like h-index, but does not explicitly mention context like citation networks, so it's not fully distinct.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus alternatives. No mention of prerequisites (e.g., graph must be a citation network) or exclusions. The description only states what it does, not when to use it.

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