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get_customer_details

Read-only

The customer's details for a ticket, loaded live from the workspace's own system through its integrations, and the buttons that can be pressed for them. You are shown only the fields a person in the workspace marked "AI can see this"; if none are, the list is empty and only they can change that, on the integration's Side panel tab. Only for a customer who was logged in.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticket_id": "<ticket_id>"
    },
    "name": "get_customer_details"
  }
}

Input Schema

NameRequiredDescriptionDefault
ticket_idYesThe ticket's id (a uuid), from list_tickets. Not its number.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior5/5

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

Annotations only declare it as a read-only, open-world operation, but the description adds substantial behavioral context: values load live from the workspace's own system via integrations, only fields marked 'AI can see this' are returned, and an empty list is a legitimate result that the agent cannot fix. That empty-result and permission-gating disclosure is exactly what an agent needs to avoid misreading a blank response.

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?

Three sentences, each doing distinct work: what is returned, the field-visibility rule with its empty case, and the logged-in precondition. Front-loaded with the resource, though the middle sentence is dense and slightly awkward.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read tool with no output schema, the description covers the return shape, the permission model, the empty-result case, where the setting is controlled, and the calling precondition. Nothing essential is missing.

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?

With schema coverage at 100%, the schema already documents ticket_id thoroughly (uuid, from list_tickets, not the number). The description adds no parameter-level detail, so baseline 3 is appropriate.

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?

States a specific resource (customer details for a ticket) and what comes back (details plus the buttons that can be pressed), so an agent can tell it apart from get_ticket or list_buttons by scope. It never names a sibling explicitly, so it stops short of full differentiation.

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?

Provides one meaningful precondition: 'Only for a customer who was logged in.' That tells the agent when the tool is applicable but gives no alternative tool to use when the condition fails, and no when-not-to-use guidance beyond that single gate.

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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