pkg-intel-mcp

bundle_size

Get the minified and gzipped bundle size and dependency count for any npm package to assess its impact on frontend performance before adoption.

Instructions

Get the minified and gzipped bundle size of an npm package (via bundlephobia), plus its dependency count. npm only — bundle size has no PyPI equivalent. Use when frontend bundle weight matters.

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": {
      "name": "<name>"
    },
    "name": "bundle_size"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes	Package name, e.g. 'express' or '@tanstack/react-query' (npm), 'requests' (PyPI)
`version`	No	Exact version, e.g. '4.17.21'. Omit to use the latest version.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Sep 8, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full transparency burden. It explains the data source (bundlephobia), the exact outputs (minified/gzipped size and dependency count), and the input expectations (npm package, optional version). No side effects are mentioned, but the read-only nature is implied and appropriate.

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, using two sentences to convey purpose, scope, and usage context. Every clause adds value; no redundant information.

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

Completeness4/5

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

Given no output schema, the description adequately specifies what is returned (bundle size, gzip size, dependency count) and the data source. It does not mention error handling or edge cases, but for a straightforward query tool this is sufficient.

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 100%, so baseline is 3. The description adds minimal parameter-specific detail beyond the schema (e.g., it reinforces the npm-only context but does not elaborate on version handling), so no increase is warranted.

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 retrieves minified and gzipped bundle size and dependency count via bundlephobia, with a specific resource (npm package). It also distinguishes itself by noting npm-only and no PyPI equivalent, which helps differentiation from siblings.

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?

Provides a clear usage condition ('when frontend bundle weight matters') and notes the npm-only scope, giving implicit comparison with siblings. However, it does not explicitly name alternative tools or when not to use it, so it's slightly less directive.

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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