Translates a lockfile diff into a human-readable upgrade plan for npm, PyPI, and GitHub Actions.
Scored across 2 tools
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Package name (e.g. 'react', 'requests') | |
| ecosystem | Yes | Package ecosystem | |
| toVersion | Yes | Target version (e.g. '19.0.0') | |
| fromVersion | Yes | Current version (e.g. '18.2.0') |
| Name | Required | Description |
|---|---|---|
| package | Yes | Package name that was analyzed |
| repoUrl | Yes | Source repository URL, or null when none could be resolved |
| ecosystem | Yes | Package ecosystem |
| toVersion | Yes | Version being upgraded to |
| fromVersion | Yes | Version being upgraded from |
| semverClass | Yes | Semver relationship between the two versions |
| releaseCount | Yes | Number of GitHub releases found strictly between the two versions |
| securityFixes | Yes | Advisories affecting fromVersion that are resolved at toVersion |
| migrationLinks | Yes | Migration or upgrade guide URLs found in release notes |
| recommendation | Yes | Single-line verdict explaining the recommendation level |
| breakingChanges | Yes | Breaking changes extracted from release notes; empty when none were found |
| releaseExcerpts | No | Raw release-note excerpts, present only as a fallback when a major/minor bump yielded no breaking changes |
| recommendationLevel | Yes | Risk classification, used to rank packages in bulk results |
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.
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.
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.
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.
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.
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.
Analyzes a list of package upgrades in parallel and returns a unified risk report with packages ranked by recommendation level (security > caution > review > likely-safe > safe). Use when the user provides many dependency changes from a Dependabot PR, npm outdated output, lockfile diff, or batch upgrade. Returns: total count, breakdown by semver class, total security fixes found, packages with breaking changes, and per-package details. Limit 50 packages per call (chunk larger lists).
| Name | Required | Description | Default |
|---|---|---|---|
| changes | Yes | List of package changes to analyze |
| Name | Required | Description |
|---|---|---|
| packages | Yes | Per-package results, ranked security > caution > review > likely-safe > safe |
| bySemverClass | Yes | Breakdown of the batch by semver class |
| totalPackages | Yes | Number of package changes submitted |
| securityFixesTotal | Yes | Total security advisories resolved across the whole batch |
| packagesWithBreakingChanges | Yes | How many packages had at least one breaking change |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds substantial behavioral detail beyond the annotations: it discloses parallel execution, the unified risk report structure, the explicit recommendation ordering (security > caution > review > likely-safe > safe), the returned aggregates, and the 50-package limit with a chunking instruction. The readOnlyHint, openWorldHint, and idempotentHint are already provided by annotations, and the description does not contradict them. This is a strong, transparent behavioral profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured: purpose first, then use case, then return content, then a practical limit. It is four sentences with no filler, and every sentence adds information an agent needs to invoke correctly or set expectations. The limit and chunking guidance is efficiently placed at the end.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one well-schematized parameter, a rich output schema, and annotations covering safety and idempotency, the description is complete. It covers what the tool does, common triggers, output content, and operational constraints (parallel, 50-item limit, chunking). Nothing an agent needs to decide between this and the singular sibling is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3; the schema already documents the 'changes' array and the ecosystem enum. The description adds contextual color by calling the items 'package upgrades' and referencing batch sources, but it does not add significant semantic detail about the fromVersion or toVersion fields, which remain self-explanatory. This meets the adequate threshold without exceeding the schema-driven baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Analyzes a list of package upgrades in parallel and returns a unified risk report with packages ranked by recommendation level.' It clearly distinguishes itself from the singular sibling tool by explicitly targeting 'many dependency changes' and listing concrete batch sources like Dependabot PRs, npm outdated output, lockfile diffs, and batch upgrades. The ranking order further clarifies the tool's specific output, leaving no ambiguity about what it does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear when-to-use context: 'Use when the user provides many dependency changes from a Dependabot PR, npm outdated output, lockfile diff, or batch upgrade.' This strongly implies that single changes belong to the sibling analyze_package_change tool, but it does not explicitly name the alternative or state a when-not condition. That is a clear usage context without formal exclusions, so it misses the top score only slightly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
analyze_package_changeanalyze_packages_bulkCheck exact npm/PyPI upgrades for evidence-backed breaking changes; query APIs and components.
Detect breaking changes, generate changelogs, diff, and validate OpenAPI specs.
Generate changelogs, release emails, help-center articles, banners, and social posts from commits.
npm/PyPI/Django dependency upgrades: security, runtime compatibility, migration, package ranking.
Add one secure layer between your agents and this server.
