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pylon_create_contact

Add new customer contacts to Pylon for support requests and portal access by providing email, name, and role details.

Instructions

Create a new customer contact in Pylon. Use this when adding a new customer who will submit support requests or access your customer portal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesContact email address. Must be valid email format. Example: "sarah@company.com"
nameYesFull name of the contact. Example: "Sarah Johnson"
portal_roleNoRole in customer portal: "admin", "member", "viewer". Determines access level. Example: "member"

Implementation Reference

  • Handler for the 'pylon_create_contact' tool - validates arguments are provided and calls pylonClient.createContact(), returning the created contact as JSON
    case 'pylon_create_contact': {
      if (!args) throw new Error('Arguments required for creating contact');
      const contact = await pylonClient.createContact(args as any);
      return {
        content: [
          {
            type: 'text',
            text: JSON.stringify(contact, null, 2),
          },
        ],
      };
    }
  • Tool registration and schema definition for 'pylon_create_contact' - defines the tool name, description, and input schema with email (required), name (required), and portal_role (optional) parameters
    {
      name: 'pylon_create_contact',
      description: 'Create a new customer contact in Pylon. Use this when adding a new customer who will submit support requests or access your customer portal.',
      inputSchema: {
        type: 'object',
        properties: {
          email: { type: 'string', description: 'Contact email address. Must be valid email format. Example: "sarah@company.com"' },
          name: { type: 'string', description: 'Full name of the contact. Example: "Sarah Johnson"' },
          portal_role: { type: 'string', description: 'Role in customer portal: "admin", "member", "viewer". Determines access level. Example: "member"' },
        },
        required: ['email', 'name'],
      },
    },
  • PylonClient method that makes the actual API POST request to /contacts endpoint to create a new contact in the Pylon API
    async createContact(contact: Omit<PylonContact, 'id'>): Promise<PylonContact> {
      const response: AxiosResponse<PylonContact> = await this.client.post('/contacts', contact);
      return response.data;
    }
  • TypeScript interface defining the PylonContact data structure with id, email, name, and optional portal_role fields
    export interface PylonContact {
      id: string;
      email: string;
      name: string;
      portal_role?: string;
    }
Behavior2/5

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

With no annotations provided, the description carries full burden. It implies a write operation ('Create') but doesn't disclose behavioral traits like required permissions, whether duplicates are allowed, error handling, or what happens on success. 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.

Conciseness5/5

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

Two sentences with zero waste: the first states the purpose, the second provides usage context. It's appropriately sized and front-loaded, with every sentence earning its place by adding value.

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 and no output schema, the description is incomplete. It covers purpose and usage but lacks behavioral details like permissions, side effects, or return values. However, it's adequate for basic understanding, aligning with a minimum viable score.

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 all parameters (email, name, portal_role) with details like format and examples. The description adds no parameter-specific information beyond what's in the schema, meeting the baseline of 3 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 action ('Create a new customer contact') and resource ('in Pylon'), with a specific purpose ('who will submit support requests or access your customer portal'). It distinguishes from siblings like 'pylon_create_issue' or 'pylon_create_team' by focusing on contacts, though it doesn't explicitly contrast with 'pylon_get_contacts' or 'pylon_search_contacts'.

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?

It provides clear context for when to use ('when adding a new customer who will submit support requests or access your customer portal'), which helps differentiate from other creation tools. However, it doesn't specify when NOT to use or explicitly name alternatives like 'pylon_update_issue' for existing contacts, keeping it from a perfect score.

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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