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pylon_create_issue

Create support tickets in Pylon to track customer problems, bug reports, or feature requests for resolution.

Instructions

Create a new support issue/ticket in Pylon. Use this to log customer problems, bug reports, or feature requests that need to be tracked and resolved.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesBrief title describing the issue. Examples: "Login page not loading", "Cannot upload files", "Billing question"
descriptionYesDetailed description of the issue, including steps to reproduce and impact. Example: "User reports that clicking login button shows error message. Affects all Chrome users on Windows."
statusYesInitial status: "open", "in_progress", "pending", "resolved", "closed". Usually "open" for new issues. Example: "open"
priorityYesPriority level: "low", "medium", "high", "urgent". Example: "high"
assigneeNoTeam member to assign (optional). Use email or user ID. Example: "support@company.com"

Implementation Reference

  • The request handler for 'pylon_create_issue' tool. Validates that arguments are provided, calls pylonClient.createIssue() with the arguments, and returns the created issue as JSON. This is the main entry point that executes when the tool is invoked.
    case 'pylon_create_issue': {
      if (!args) throw new Error('Arguments required for creating issue');
      const issue = await pylonClient.createIssue(args as any);
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(issue, null, 2),
          },
        ],
      };
    }
  • The createIssue method in PylonClient class that performs the actual API call. Makes an HTTP POST request to '/issues' endpoint with the issue data and returns the created PylonIssue object from the API response.
    async createIssue(issue: Omit<PylonIssue, 'id'>): Promise<PylonIssue> {
      const response: AxiosResponse<PylonIssue> = await this.client.post('/issues', issue);
      return response.data;
    }
  • Tool registration and input schema definition for 'pylon_create_issue'. Defines the tool name, description, and inputSchema with properties: title (required), description (required), status (required), priority (required), and assignee (optional).
      name: 'pylon_create_issue',
      description: 'Create a new support issue/ticket in Pylon. Use this to log customer problems, bug reports, or feature requests that need to be tracked and resolved.',
      inputSchema: {
        type: 'object',
        properties: {
          title: { type: 'string', description: 'Brief title describing the issue. Examples: "Login page not loading", "Cannot upload files", "Billing question"' },
          description: { type: 'string', description: 'Detailed description of the issue, including steps to reproduce and impact. Example: "User reports that clicking login button shows error message. Affects all Chrome users on Windows."' },
          status: { type: 'string', description: 'Initial status: "open", "in_progress", "pending", "resolved", "closed". Usually "open" for new issues. Example: "open"' },
          priority: { type: 'string', description: 'Priority level: "low", "medium", "high", "urgent". Example: "high"' },
          assignee: { type: 'string', description: 'Team member to assign (optional). Use email or user ID. Example: "support@company.com"' },
        },
        required: ['title', 'description', 'status', 'priority'],
      },
    },
  • PylonIssue TypeScript interface defining the structure of an issue object with fields: id, title, description, status, priority, and optional assignee.
    export interface PylonIssue {
      id: string;
      title: string;
      description: string;
      status: string;
      priority: string;
      assignee?: string;
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that issues are for tracking and resolution, which implies persistence and workflow, but lacks details on permissions required, rate limits, whether creation is idempotent, or what happens on success/failure. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and usage context without any wasted words. Every sentence earns its place by providing essential information.

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 complexity (a mutation operation with 5 parameters) and the absence of both annotations and an output schema, the description is moderately complete. It covers the basic purpose and usage but lacks details on behavioral aspects like error handling or return values, which are important for a creation tool without structured output documentation.

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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema itself. The description does not add any additional meaning or context about the parameters beyond what's already in the schema, such as explaining relationships between fields or usage tips. Baseline 3 is appropriate when the schema does the heavy lifting.

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 clearly states the tool's purpose with a specific verb ('Create') and resource ('new support issue/ticket in Pylon'), and distinguishes it from siblings by specifying it's for logging customer problems, bug reports, or feature requests. This differentiates it from other create tools like pylon_create_contact or pylon_create_team.

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?

The description provides clear context on when to use this tool ('to log customer problems, bug reports, or feature requests that need to be tracked and resolved'), which helps guide usage. However, it doesn't explicitly mention when not to use it or name specific alternatives among the sibling tools, such as pylon_update_issue for modifying existing issues.

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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