Skip to main content
Glama
nulab

Backlog MCP Server

get_issue_types

Retrieve available issue types for a Backlog project to categorize and organize tasks effectively. Specify project ID or key to get relevant issue type options.

Instructions

Returns list of issue types for a project

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
projectIdNoThe numeric ID of the project (e.g., 12345)
projectKeyNoThe key of the project (e.g., 'PROJECT')

Implementation Reference

  • The core handler function that executes the tool logic: resolves the project ID or key using resolveIdOrKey utility and calls the Backlog client's getIssueTypes method.
    handler: async ({ projectId, projectKey }) => {
      const result = resolveIdOrKey(
        'project',
        { id: projectId, key: projectKey },
        t
      );
      if (!result.ok) {
        throw result.error;
      }
      return backlog.getIssueTypes(result.value);
    },
  • Input schema for the tool, defining optional projectId (number) or projectKey (string) parameters with descriptions.
    const getIssueTypesSchema = buildToolSchema((t) => ({
      projectId: z
        .number()
        .optional()
        .describe(
          t(
            'TOOL_GET_GIT_REPOSITORIES_PROJECT_ID',
            'The numeric ID of the project (e.g., 12345)'
          )
        ),
      projectKey: z
        .string()
        .optional()
        .describe(
          t(
            'TOOL_GET_GIT_REPOSITORIES_PROJECT_KEY',
            "The key of the project (e.g., 'PROJECT')"
          )
        ),
    }));
  • Exports the getIssueTypesTool factory function that constructs the complete ToolDefinition object for the 'get_issue_types' tool, including name, description, input/output schemas, important fields, and handler.
    export const getIssueTypesTool = (
      backlog: Backlog,
      { t }: TranslationHelper
    ): ToolDefinition<
      ReturnType<typeof getIssueTypesSchema>,
      (typeof IssueTypeSchema)['shape']
    > => {
      return {
        name: 'get_issue_types',
        description: t(
          'TOOL_GET_ISSUE_TYPES_DESCRIPTION',
          'Returns list of issue types for a project'
        ),
        schema: z.object(getIssueTypesSchema(t)),
        outputSchema: IssueTypeSchema,
        importantFields: ['id', 'name'],
        handler: async ({ projectId, projectKey }) => {
          const result = resolveIdOrKey(
            'project',
            { id: projectId, key: projectKey },
            t
          );
          if (!result.ok) {
            throw result.error;
          }
          return backlog.getIssueTypes(result.value);
        },
      };
    };
  • Registers the get_issue_types tool by calling getIssueTypesTool and adding it to the 'issue' toolset group in the allTools function.
    getIssueTypesTool(backlog, helper),
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 only states the basic action without mentioning critical details like whether the operation is read-only, requires authentication, has rate limits, or what the return format looks like (e.g., list structure, pagination). This leaves significant gaps in understanding the tool's 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 with no wasted words, making it easy to parse and front-loaded with the core action. It avoids redundancy and stays focused on the essential purpose, earning full marks for conciseness.

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?

Given the tool's moderate complexity (a read operation with two parameters) and no annotations or output schema, the description is incomplete. It lacks details on behavioral traits, return values, error handling, or usage context, which are necessary for an AI agent to invoke it correctly and handle responses 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?

The input schema has 100% description coverage, with clear documentation for both parameters (projectId and projectKey). The description does not add any meaning beyond what the schema provides, such as explaining parameter relationships or usage examples. According to the rules, with high schema coverage, the baseline score is 3, as the schema adequately handles parameter semantics.

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 ('Returns list') and resource ('issue types for a project'), making the purpose specific and understandable. However, it does not distinguish this tool from similar siblings like 'get_categories' or 'get_priorities', which also return lists of metadata for projects, leaving some ambiguity about its unique role.

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, such as whether it's for retrieving issue types before creating issues or for filtering issues. With many sibling tools available, the lack of context on usage scenarios or prerequisites makes it minimally helpful for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nulab/backlog-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server