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pylon_create_tag

Create new tags to categorize and organize support tickets in Pylon's customer support platform, enabling effective filtering and management of issues and contacts.

Instructions

Create a new tag for categorizing issues and contacts. Use this to add new categories that help organize and filter your support tickets effectively.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesTag name that describes the category. Examples: "billing-question", "feature-request", "bug-report", "urgent", "enterprise-customer"
colorNoColor for the tag in hex format or color name. Examples: "#FF0000", "red", "#00AA00", "blue"

Implementation Reference

  • Handler for 'pylon_create_tag' tool that validates arguments are present and calls pylonClient.createTag(), returning the created tag as JSON text content
    case 'pylon_create_tag': {
      if (!args) throw new Error('Arguments required for creating tag');
      const tag = await pylonClient.createTag(args as any);
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(tag, null, 2),
          },
        ],
      };
    }
  • Implementation of createTag method in PylonClient class that makes a POST request to /tags endpoint and returns the created tag
    async createTag(tag: Omit<PylonTag, 'id'>): Promise<PylonTag> {
      const response: AxiosResponse<PylonTag> = await this.client.post('/tags', tag);
      return response.data;
    }
  • Type definition for PylonTag interface with required id and name fields, and optional color field
    export interface PylonTag {
      id: string;
      name: string;
      color?: string;
    }
  • src/index.ts:291-300 (registration)
    Tool registration defining the tool name, description, and input schema with required 'name' property and optional 'color' property
    name: 'pylon_create_tag',
    description: 'Create a new tag for categorizing issues and contacts. Use this to add new categories that help organize and filter your support tickets effectively.',
    inputSchema: {
      type: 'object',
      properties: {
        name: { type: 'string', description: 'Tag name that describes the category. Examples: "billing-question", "feature-request", "bug-report", "urgent", "enterprise-customer"' },
        color: { type: 'string', description: 'Color for the tag in hex format or color name. Examples: "#FF0000", "red", "#00AA00", "blue"' },
      },
      required: ['name'],
    },
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. It states this is a creation tool, implying mutation, but doesn't disclose behavioral traits like required permissions, whether tags are unique, rate limits, or what happens on duplicate names. This is a significant gap for a mutation tool with zero annotation coverage.

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 appropriately sized with two sentences that are front-loaded (purpose first, then usage). Every sentence adds value, though it could be slightly more concise by combining ideas.

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 a mutation tool with no annotations, no output schema, and 2 parameters, the description is minimally adequate. It covers purpose and basic usage but lacks details on behavior, permissions, or output, leaving gaps for an AI agent to invoke it correctly in complex scenarios.

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 already documents both parameters (name and color) with examples. The description doesn't add meaning beyond what the schema provides, such as constraints or usage context for parameters. Baseline 3 is appropriate when schema does the heavy lifting.

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 resource ('new tag'), specifying it's for categorizing issues and contacts. It distinguishes from siblings like pylon_create_contact or pylon_create_issue by focusing on tags, but doesn't explicitly differentiate from pylon_get_tags (a read operation).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for organizing support tickets, suggesting when to use it (to add categories for filtering). However, it doesn't provide explicit alternatives (e.g., when to use pylon_get_tags instead) or exclusions (e.g., not for modifying existing tags).

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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