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BumpGuard

BumpGuard

by appcreationsca
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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check_upgrade

Analyzes your code against API differences between dependency versions to report breaking changes with line numbers and fix hints.

Instructions

Check what breaks in YOUR code when you upgrade a dependency.

Call this BEFORE bumping a dependency version. It extracts the real public API of the currently-installed (or from_version) package and the target to_version, diffs them, then scans the provided code to report exactly which of your usages break — with line numbers, severity, and fix hints.

Args: package: Distribution name to upgrade (e.g. "pandas"). to_version: The version you want to move to (e.g. "2.2.0"). code: The source code that uses the package (a file or snippet). from_version: Optional baseline version; defaults to what is installed. language: Ecosystem provider id. Default "python".

Returns a report with safe_to_upgrade, a severity summary, and per-line findings. Note: analysis is static, so "no findings" means "nothing proven to break", not a guarantee.

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": {
      "package": "<package>",
      "to_version": "<to_version>",
      "code": "<code>"
    },
    "name": "check_upgrade"
  }
}

Input Schema

NameRequiredDescriptionDefault
packageYes
to_versionYes
codeYes
from_versionNo
languageNopython

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: static analysis, extraction of public API, diffing, scanning user code, and a disclaimer that 'no findings' is not a guarantee. This fully informs the agent of the tool's behavior and limitations.

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 and well-structured: a one-line intro, a short paragraph on usage, a bulleted list of arguments, and a returns note. Every sentence adds value, and there is no redundant or verbose text.

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

Completeness5/5

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

Given the tool's complexity (5 parameters, no output schema, static analysis), the description covers all necessary aspects: purpose, usage timing, parameter details, return format (report with fields), and limitations. It is sufficiently complete for an agent to select and invoke the tool correctly.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description clearly explains each parameter in the Args section: package, to_version, code, from_version (optional, defaults to installed), and language (default 'python'). It adds meaningful context beyond the parameter names and types.

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's purpose: 'Check what breaks in YOUR code when you upgrade a dependency.' It uses a specific verb-resource combination and distinctly positions itself among siblings like check_import, diff_versions, etc., which are about different aspects of dependency management.

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

Usage Guidelines5/5

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

The description explicitly advises 'Call this BEFORE bumping a dependency version,' providing clear when-to-use guidance. It also includes a caveat about static analysis limitations, helping set expectations. While it doesn't explicitly list alternatives, the context and sibling names imply when not to use it.

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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