github-pr-review-mcp

review_pr_with_prompt

Retrieve formatted pull request details and review instructions to generate a comprehensive AI code review.

Instructions

Get PR context and review prompt for AI-powered code review. Returns formatted PR data and review guidelines that can be used with an LLM to generate a comprehensive review. The LLM can then use submit_pr_review to submit the review.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "owner": "<owner>",
      "repo": "<repo>",
      "prNumber": "<prNumber>"
    },
    "name": "review_pr_with_prompt"
  }
}

Input Schema

Name	Required	Description
`repo`	Yes	Repository name
`owner`	Yes	Repository owner/organization
`prNumber`	Yes	Pull request number
`customPrompt`	No	Optional custom review prompt. If not provided, uses default PR review guidelines.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.4Aug 30, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden. The verbs 'get' and 'returns' convey a read-only retrieval operation, and the explicit handoff to submit_pr_review indicates that no submission happens in this call. Auth and error behavior are not mentioned, but the core behavior is reasonably transparent.

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?

Two focused sentences front-load the tool's purpose, describe what it returns, and name the downstream action. There is no filler or unnecessary repetition.

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?

The description is sufficient for an agent to understand when to call this tool and what to do next. The main gap is the lack of detail about what 'formatted PR data' actually contains, which is more noticeable because there is no output schema.

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 parameters are already well documented in the input schema. The description adds no additional parameter-level meaning, matching the baseline for high schema coverage.

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 and resource: get PR context and a review prompt for AI-powered code review. It also distinguishes this tool from submission tooling by explicitly pointing to submit_pr_review as the follow-up step.

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?

It clearly establishes the intended workflow: retrieve context and review guidelines here, then submit via submit_pr_review. It does not explicitly explain when to choose this over siblings like get_pr_details or analyze_pr_code, so it stops short of a full 5.

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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