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pylon_create_issue

Create a new support ticket in Pylon to track customer issues with specified title, description, priority, and assignee.

Instructions

Create a new issue/ticket in Pylon

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesTitle of the issue
body_htmlYesHTML content of the issue body
account_idNoAssociated account ID
assignee_idNoUser ID to assign the issue to
contact_idNoAssociated contact ID
requester_idNoRequester contact ID
tagsNoTags to apply
priorityNoIssue priority

Implementation Reference

  • src/index.ts:682-707 (registration)
    Registration of the 'pylon_create_issue' tool via server.tool(), including Zod schema for input validation and the async handler callback.
    server.tool(
    	'pylon_create_issue',
    	'Create a new issue/ticket in Pylon',
    	{
    		title: z.string().describe('Title of the issue'),
    		body_html: z.string().describe('HTML content of the issue body'),
    		account_id: z.string().optional().describe('Associated account ID'),
    		assignee_id: z
    			.string()
    			.optional()
    			.describe('User ID to assign the issue to'),
    		contact_id: z.string().optional().describe('Associated contact ID'),
    		requester_id: z.string().optional().describe('Requester contact ID'),
    		tags: z.array(z.string()).optional().describe('Tags to apply'),
    		priority: z
    			.enum(['urgent', 'high', 'medium', 'low'])
    			.optional()
    			.describe('Issue priority'),
    	},
    	async (params) => {
    		const result = await client.createIssue(params);
    		return {
    			content: [{ type: 'text', text: JSON.stringify(result.data, null, 2) }],
    		};
    	},
    );
  • Handler function for pylon_create_issue: calls client.createIssue(params) and returns the result data as JSON.
    	async (params) => {
    		const result = await client.createIssue(params);
    		return {
    			content: [{ type: 'text', text: JSON.stringify(result.data, null, 2) }],
    		};
    	},
    );
  • Zod schema defining input parameters: title (required string), body_html (required string), and optional fields account_id, assignee_id, contact_id, requester_id, tags (array of strings), priority (enum).
    {
    	title: z.string().describe('Title of the issue'),
    	body_html: z.string().describe('HTML content of the issue body'),
    	account_id: z.string().optional().describe('Associated account ID'),
    	assignee_id: z
    		.string()
    		.optional()
    		.describe('User ID to assign the issue to'),
    	contact_id: z.string().optional().describe('Associated contact ID'),
    	requester_id: z.string().optional().describe('Requester contact ID'),
    	tags: z.array(z.string()).optional().describe('Tags to apply'),
    	priority: z
    		.enum(['urgent', 'high', 'medium', 'low'])
    		.optional()
    		.describe('Issue priority'),
    },
  • PylonClient.createIssue() method that sends a POST request to /issues endpoint with the provided data. Accepts title, body_html, and optional fields like account_id, assignee_id, contact_id, requester_id, tags, priority, etc.
    async createIssue(data: {
    	title: string;
    	body_html: string;
    	account_id?: string;
    	assignee_id?: string;
    	contact_id?: string;
    	requester_id?: string;
    	user_id?: string;
    	tags?: string[];
    	attachment_urls?: string[];
    	custom_fields?: object[];
    	priority?: 'urgent' | 'high' | 'medium' | 'low';
    	destination_metadata?: object;
    }): Promise<SingleResponse<Issue>> {
    	return this.request<SingleResponse<Issue>>('POST', '/issues', data);
    }
Behavior2/5

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

No annotations are present, so the description must disclose behavioral traits. It only states 'Create a new issue/ticket' without mentioning side effects, authentication needs, rate limits, or any constraints. For a create operation, some behavioral context is expected.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, very concise. It avoids unnecessary words, though it could be slightly more informative without losing 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?

Despite having 8 parameters and no output schema, the description does not explain return values, required permissions, or parameter usage context. The description is too minimal for the complexity of the tool.

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?

All parameters have descriptions in the input schema (100% coverage), so the baseline is 3. The description adds no additional meaning beyond what the schema already provides.

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 'Create' and the resource 'issue/ticket', making the purpose obvious. However, it does not distinguish from sibling create tools like pylon_create_account.

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 such as pylon_update_issue or other create tools. No context about prerequisites or typical use cases.

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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