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wkoutre

Linear MCP Server

by wkoutre

linear_getLabels

Retrieve issue labels from Linear to categorize and organize project tasks. This tool helps users manage workflow by accessing available label options.

Instructions

Get a list of issue labels from Linear

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The handler function for linear_getLabels tool. It wraps the linearService.getLabels() call with error handling.
    export function handleGetLabels(linearService: LinearService) {
      return async (args: unknown) => {
        try {
          return await linearService.getLabels();
        } catch (error) {
          logError("Error getting labels", error);
          throw error;
        }
      };
    } 
  • The schema definition for the linear_getLabels tool, including input (empty) and output schema for labels.
    export const getLabelsToolDefinition: MCPToolDefinition = {
      name: "linear_getLabels",
      description: "Get a list of issue labels from Linear",
      input_schema: {
        type: "object",
        properties: {},
      },
      output_schema: {
        type: "array",
        items: {
          type: "object",
          properties: {
            id: { type: "string" },
            name: { type: "string" },
            description: { type: "string" },
            color: { type: "string" },
            team: {
              type: "object",
              properties: {
                id: { type: "string" },
                name: { type: "string" }
              }
            }
          }
        }
      }
    }; 
  • Registration of the linear_getLabels tool handler in the tool handlers map.
    linear_getLabels: handleGetLabels(linearService),
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 of behavioral disclosure. It states this is a 'Get' operation, implying it's read-only, but doesn't specify if it requires authentication, has rate limits, returns paginated results, or what the output format looks like. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 directly states the tool's function without any fluff or redundancy. It's appropriately sized for a simple retrieval tool and front-loads the key information ('Get a list of issue labels'), making it easy to scan and understand quickly.

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

Completeness3/5

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose but lacks details on output format, authentication needs, or usage context, which could be helpful for an AI agent. It's complete enough for a basic list tool but doesn't provide rich contextual information.

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 parameters with 100% coverage, so there's no need for parameter documentation in the description. The description correctly avoids discussing parameters, focusing instead on the tool's purpose. This meets the baseline for tools with no parameters, as it doesn't mislead or omit necessary details.

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 verb ('Get') and resource ('a list of issue labels from Linear'), making the purpose immediately understandable. It distinguishes this from other sibling tools that manipulate labels (like linear_addIssueLabel or linear_removeIssueLabel) by focusing on retrieval. However, it doesn't specify if this returns all labels or filtered ones, which prevents a perfect 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for filtering issues or setting up new ones), or compare it to other list tools like linear_getIssues or linear_getProjects. Without such context, users 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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