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dep-diff-mcp

Analyze a single dependency version change

analyze_package_change
Read-onlyIdempotent

Given one package and two versions (from -> to), returns a structured upgrade analysis: semver classification, GitHub release notes summary, detected breaking changes, security advisories fixed in the range, migration guide links, and a clear recommendation. Use when the user asks about a specific package upgrade ('what changed between react 18 and 19', 'is it safe to bump axios from 0.27 to 1.0', 'what does upgrading lodash 4.17.20 to 4.17.21 fix'). Supports npm, pypi, and github-actions (use the action reference as the name, e.g. actions/checkout). For analyzing many packages at once or a Dependabot batch, use analyze_packages_bulk instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPackage name (e.g. 'react', 'requests')
ecosystemYesPackage ecosystem
toVersionYesTarget version (e.g. '19.0.0')
fromVersionYesCurrent version (e.g. '18.2.0')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
packageYesPackage name that was analyzed
repoUrlYesSource repository URL, or null when none could be resolved
ecosystemYesPackage ecosystem
toVersionYesVersion being upgraded to
fromVersionYesVersion being upgraded from
semverClassYesSemver relationship between the two versions
releaseCountYesNumber of GitHub releases found strictly between the two versions
securityFixesYesAdvisories affecting fromVersion that are resolved at toVersion
migrationLinksYesMigration or upgrade guide URLs found in release notes
recommendationYesSingle-line verdict explaining the recommendation level
breakingChangesYesBreaking changes extracted from release notes; empty when none were found
releaseExcerptsNoRaw release-note excerpts, present only as a fallback when a major/minor bump yielded no breaking changes
recommendationLevelYesRisk classification, used to rank packages in bulk results

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, and non-destructive behavior, so the description does not need to restate those. It adds useful behavioral context by enumerating the analysis components returned (semver classification, release notes, breaking changes, advisories, migration links, recommendation) and by specifying supported ecosystems and the github-actions naming convention. No contradiction with annotations is present.

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?

Three sentences carry the full message with no filler: the core action and output are front-loaded, examples make the intended queries concrete, and the sibling tool handoff is compressed into a single clause. Every sentence earns its place.

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?

The description provides all essential invocation context: required inputs, output content, supported ecosystems, and when to choose the sibling tool instead. Output schema and annotations already cover return structure and safety semantics, so nothing critical is missing for correct selection and invocation.

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

Parameters4/5

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

Schema coverage is 100% and each parameter has a basic description, so the baseline is solid. The description adds meaningful semantic detail beyond the schema: concrete version examples and the rule that github-actions names should use the action reference like 'actions/checkout'. This extra guidance helps an agent supply correctly formatted parameters.

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 states a specific verb ('returns a structured upgrade analysis') and identifies the exact resource: one package plus a from/to version pair. It clearly distinguishes itself from the sibling analyze_packages_bulk by emphasizing the single-dependency scope. There is no ambiguity about what the tool does.

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 gives the trigger condition: use when the user asks about a specific package upgrade, backed by concrete example queries. It also names the alternative analyze_packages_bulk for many packages or Dependabot batches, making the routing decision unambiguous.

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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TDQS

A4.4/5.0
Disambiguation5/5

The two tools split cleanly by cardinality: one analyzes a single package upgrade, and the other handles batches of upgrades. Their descriptions even cross-reference each other to route the agent correctly, so there is no overlap or ambiguity.

Naming Consistency4/5

Both tools share an 'analyze_package(s)_...' verb-noun prefix and lowercase snake_case convention. The slight asymmetry between 'package_change' (singular) and 'packages_bulk' (plural + suffix) is minor but predictable.

Tool Count3/5

With only two tools, the server is on the thin side of the typical 3-15 tool range. Both tools are substantial and cover a focused workflow, which makes the count understandable, but it still feels minimal for a dependency-diff server.

Completeness4/5

The single/bulk split covers the core upgrade-analysis workflow without dead ends, since the bulk tool handles many packages and the single tool provides depth. Minor gaps exist around lockfile or dependency-tree diffing, but agents can work around them by feeding package lists to the bulk tool.