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Enferlain

antigravity-review-mcp

by Enferlain

review_with_context

Review code changes against project context by specifying diff target and optional context or focus files to generate a targeted code review.

Instructions

Review code changes against project context using GLM.

Args: diff_target: 'staged', 'unstaged', or a git ref like 'HEAD~1' context_files: Additional files or OpenSpec change folders to read focus_files: Specific files to focus the review on task_description: Optional task description for reviewer intent working_directory: Git repository root to review. Required unless the server was started with --workspace-dir include_trace: Include a compact diagnostic trace in the returned review

Returns: The generated code review.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diff_targetNostaged
focus_filesNo
context_filesNo
include_traceNo
task_descriptionNo
working_directoryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions 'using GLM' but does not disclose behavioral traits like destructiveness, authentication needs, rate limits, or data handling. The description is minimal on behavioral context beyond the review generation.

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 a well-structured docstring with a one-line summary followed by Args and Returns sections. It is concise, with each sentence earning its place, though the parameter list could be slightly more compact.

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 6 parameters and no annotations, the description covers each parameter's purpose and mentions the return value. While it doesn't explain error cases or prerequisites, the presence of an output schema partly compensates. Overall, it provides sufficient context for basic usage.

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?

The input schema has 0% description coverage, so the description's parameter details are crucial. It explains diff_target options, the nature of context_files and focus_files, the working_directory requirement, and include_trace purpose. This adds significant meaning beyond the schema 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 'Review code changes against project context using GLM,' which is a specific verb and resource. It distinguishes the tool as a code review tool, and with no siblings provided, it stands alone effectively.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives, but it implies use for code review. There are no sibling tools to differentiate, so the lack of exclusion guidance is acceptable but not proactive.

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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