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create_issue_labels

Add labels to issues in AtomGit repositories to organize, categorize, and prioritize tasks for better project management.

Instructions

Add labels to an issue in a repository

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ownerYesRepository owner, typically referred to as 'username'. Case-insensitive.
repoYesRepository name. Case-insensitive.
issue_numberYesIssue number
labelsYesArray of label names

Implementation Reference

  • The main handler function that sends a POST request to the AtomGit API to add labels to a specific issue.
    export async function createIssueLabels(
      owner: string,
      repo: string,
      issue_number: number,
      labels: string[]
    ) {
      return atomGitRequest(
        `https://api.atomgit.com/repos/${encodeURIComponent(owner)}/${encodeURIComponent(repo)}/issues/${encodeURIComponent(issue_number)}/labels`,
        {
          method: "POST",
          body: labels,
        }
      );
    }
  • Zod schema defining the input parameters for the create_issue_labels tool.
    export const CreateIssueLabelsSchema = z.object({
      owner: z.string().describe("Repository owner, typically referred to as 'username'. Case-insensitive."),
      repo: z.string().describe("Repository name. Case-insensitive."),
      issue_number: z.number().describe("Issue number"),
      labels: z.array(z.string()).describe("Array of label names"),
    });
  • index.ts:183-186 (registration)
    Tool registration in the MCP server's list of tools, including name, description, and input schema.
      name: "create_issue_labels",
      description: "Add labels to an issue in a repository",
      inputSchema: zodToJsonSchema(label.CreateIssueLabelsSchema),
    },
  • index.ts:465-473 (registration)
    Dispatcher case in the CallToolRequestHandler that parses arguments and calls the createIssueLabels handler.
    case "create_issue_labels": {
      const args = label.CreateIssueLabelsSchema.parse(request.params.arguments);
      const { owner, repo, issue_number, labels } = args;
    
      const result = await label.createIssueLabels(owner, repo, issue_number, labels);
      return {
        content: [{ type: "text", text: JSON.stringify(result, 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 but only states the basic action without disclosing behavioral traits like whether it overwrites existing labels, requires specific permissions, handles errors, or has rate limits. This is inadequate for a mutation tool.

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 with zero waste, clearly front-loading the core action. It's appropriately sized for the tool's complexity.

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 mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavior, error handling, or return values, leaving significant gaps for an AI agent to operate effectively.

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 schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema, such as format examples or constraints, meeting the baseline for high coverage.

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 ('Add labels') and target ('to an issue in a repository'), making the purpose evident. However, it doesn't differentiate from sibling tools like 'delete_issue_label' or 'get_issue_labels', which would require explicit comparison to earn a 5.

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 alternatives like 'delete_issue_label' or 'get_issue_labels', nor does it mention prerequisites such as issue existence or label availability. The description lacks context for decision-making.

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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