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find_breaking_changes

Diff your .NET solution's public API against a baseline to detect breaking and non-breaking changes. Get a sorted list with severity, name, and location.

Instructions

Diff the current solution's public API surface against a baseline (JSON snapshot from a prior get_public_api_surface run, or a baseline .dll file). Reports five change kinds: Removed/KindChanged/AccessibilityNarrowed (Breaking) plus Added/AccessibilityWidened (NonBreaking). Returns a summary plus a per-change list (kind, severity, fully-qualified name, entity kind, project, file, line, details). Sort: Breaking before NonBreaking, then name ASC. Limitations: return type changes, sealed-ness changes, and nullable annotation changes are not detected (PublicApiEntry schema doesn't capture them).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baselinePathYesPath to a baseline .json snapshot (from a prior get_public_api_surface call) or a baseline .dll file.
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the tool's read-only nature (diffing), reports five change kinds with breaking/non-breaking classification, sorting order, and states limitations (e.g., not detecting return type changes). It does not mention side effects or performance, but the disclosure is substantial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured, starting with the main function, then listing change kinds, return structure, sorting, and limitations. It is detailed but not verbose. Minor redundancy exists (e.g., repeating 'from a prior get_public_api_surface'), but overall it is efficient and front-loaded.

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 the complexity (multiple change kinds, sorting, limitations) and lack of output schema, the description covers the return fields and behavior thoroughly. It mentions limitations and required input type. It does not specify if the solution must be loaded or the output format, but these are inferable. Adequate for the tool's complexity.

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?

The input schema has one parameter with a detailed description covering both format and source. Since schema description coverage is 100%, the baseline score is 3. The tool description repeats some schema information but does not add significant new meaning beyond what the schema already provides.

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: diffing the current public API surface against a baseline, listing five specific change kinds, and returning a structured result. It uses a specific verb ('diff') and resource ('public API surface'), and the detail distinguishes it from sibling tools like get_public_api_surface.

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?

The description provides context that a baseline from a prior get_public_api_surface run is required, but it does not explicitly state when to use this tool versus alternatives or when not to use it. The exclusion of alternative guidance is a minor gap, but the usage context is clear enough.

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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