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PhialsBasement

GitHub MCP Server Plus

create_pull_request

Create a new pull request in a GitHub repository to propose and merge code changes between branches.

Instructions

Create a new pull request in a GitHub repository

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerYesRepository owner (username or organization)
repoYesRepository name
titleYesPull request title
bodyNoPull request body/description
headYesThe name of the branch where your changes are implemented
baseYesThe name of the branch you want the changes pulled into
draftNoWhether to create the pull request as a draft
maintainer_can_modifyNoWhether maintainers can modify the pull request

Implementation Reference

  • The core handler function that executes the create_pull_request tool by making a POST request to the GitHub API to create a pull request.
    export async function createPullRequest(
      params: z.infer<typeof CreatePullRequestSchema>
    ): Promise<z.infer<typeof GitHubPullRequestSchema>> {
      const { owner, repo, ...options } = CreatePullRequestSchema.parse(params);
    
      const response = await githubRequest(
        `https://api.github.com/repos/${owner}/${repo}/pulls`,
        {
          method: "POST",
          body: options,
        }
      );
    
      return GitHubPullRequestSchema.parse(response);
    }
  • Zod schema defining the input parameters for the create_pull_request tool, used for validation.
    export const CreatePullRequestSchema = z.object({
      owner: z.string().describe("Repository owner (username or organization)"),
      repo: z.string().describe("Repository name"),
      title: z.string().describe("Pull request title"),
      body: z.string().optional().describe("Pull request body/description"),
      head: z.string().describe("The name of the branch where your changes are implemented"),
      base: z.string().describe("The name of the branch you want the changes pulled into"),
      draft: z.boolean().optional().describe("Whether to create the pull request as a draft"),
      maintainer_can_modify: z.boolean().optional().describe("Whether maintainers can modify the pull request")
    });
  • index.ts:103-107 (registration)
    Registration of the create_pull_request tool in the list returned by ListToolsRequestHandler, including name, description, and input schema reference.
    {
      name: "create_pull_request",
      description: "Create a new pull request in a GitHub repository",
      inputSchema: zodToJsonSchema(pulls.CreatePullRequestSchema),
    },
  • Dispatch handler in the main CallToolRequestHandler switch that parses arguments and delegates to the pulls.createPullRequest implementation.
    case "create_pull_request": {
      const args = pulls.CreatePullRequestSchema.parse(request.params.arguments);
      const pullRequest = await pulls.createPullRequest(args);
      return {
        content: [{ type: "text", text: JSON.stringify(pullRequest, null, 2) }],
      };
    }
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure but offers minimal information. It states the tool creates a pull request but doesn't mention authentication requirements, rate limits, what happens on success/failure, whether it triggers notifications, or if it's idempotent. For a write operation with zero annotation coverage, this leaves critical behavioral traits undocumented.

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?

The description is a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly. Every word earns its place by conveying the essential action and target.

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

Completeness2/5

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

For a complex write operation with 8 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, error conditions, or behavioral nuances like GitHub-specific constraints. The high parameter count and mutation nature demand more contextual information than provided.

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%, providing clear documentation for all 8 parameters. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3. However, it doesn't compensate for any gaps since there are none in the schema.

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 action ('create') and resource ('pull request in a GitHub repository'), making the purpose immediately understandable. However, it doesn't distinguish this tool from sibling tools like create_issue or create_repository, which would require mentioning it's specifically for code review workflows between branches.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing existing branches), contrast with create_issue for non-code changes, or specify when draft vs. regular pull requests are appropriate. Without this context, agents must infer usage from the tool name alone.

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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