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generate_pr_review

Generates AI-powered code reviews for GitHub pull requests, analyzing diffs and incorporating project context from guidelines and configuration files.

Instructions

Generate code review for a GitHub Pull Request with configuration discovery.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
github_pr_urlNoGitHub PR URL (e.g., 'https://github.com/owner/repo/pull/123')
project_pathNoOptional local project path for context (default: current directory)
temperatureNoTemperature for AI model (default: 0.5, range: 0.0-2.0)
enable_gemini_reviewNoEnable Gemini AI code review generation (default: true)
include_claude_memoryNoInclude CLAUDE.md files in context (default: true)
include_cursor_rulesNoInclude Cursor rules files in context (default: false)
auto_meta_promptNoAutomatically generate and embed meta prompt in user_instructions (default: true)
use_templated_instructionsNoUse templated backup instructions instead of generated meta prompt (default: false)
create_context_fileNoSave context to file and return context content (default: false)
raw_context_onlyNoReturn raw context content without AI processing (default: false)
text_outputNoReturn content directly without saving (default: false - saves to timestamped .md file)
thinking_budgetNoOptional token budget for thinking mode (if supported by model)
url_contextNoOptional URL(s) to include in context - can be string or list of strings

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior1/5

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

With no annotations, the description must disclose behavioral traits. It only states 'Generate code review... with configuration discovery' without explaining side effects, authentication needs, rate limits, or output format. This is a critical gap.

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

Conciseness3/5

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

The description is a single sentence, which is concise, but it lacks structure and front-loads no key information beyond the tool name. It is not optimally organized for an AI agent.

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

Completeness1/5

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

Given the tool's complexity (13 parameters, multiple flags), the description is severely incomplete. It omits prerequisites (e.g., GitHub access), output details, and ignores the output schema. The agent cannot judge completeness.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema, only mentioning 'configuration discovery' vaguely. It does not enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: generating a code review for a GitHub Pull Request. However, it does not differentiate from the sibling tool 'generate_ai_code_review', preventing a top score.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus the siblings 'ask_gemini' or 'generate_ai_code_review'. The description lacks any context for appropriate usage.

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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