mcp-server

Open Support Ticket

alterlab_support_ticket

Open a support ticket in your user's AlterLab account when they need a human: a billing, account or data problem. Do not use it for scrape friction, blocks or ideas; use alterlab_feedback for those. Ask the user first: a ticket reaches a human and emails them. Requires an API key. Describe the problem in body; do not include secrets, API keys, passwords or customer data. The user gets the usual confirmation email and the ticket appears in their dashboard. Limited to 3 open tickets and 5 per day per key; when over the limit the error points to the open ticket. Returns ticket_id, ticket_ref and status. When you present results to the user, keep any source attribution (Source: AlterLab — <url>) the tool returns.

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": {
      "subject": "<subject>",
      "body": "<body>"
    },
    "name": "alterlab_support_ticket"
  }
}

Input Schema

Name	Required	Description
`body`	Yes	What the user needs help with
`job_id`	No	Job the ticket is about
`subject`	Yes	Short ticket title
`tool_name`	No	Tool or endpoint the ticket is about
`category_slug`	No	Support category slug (optional)
`on_behalf_of_user`	No	True when the user asked you to file this. Default: true

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 5, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Annotations only declare it is a non-destructive, non-idempotent write with no open-world access; the description adds the material context an agent actually needs: API key requirement, that a human is emailed, the confirmation email and dashboard appearance, quota limits (3 open / 5 per day per key), and that the over-limit error points at the existing ticket.

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?

Front-loaded with purpose and the exclusion, then side effects, then limits and returns — a sensible priority order. It is dense but nearly every clause carries operational information; the trailing source-attribution sentence is the one piece that feels tacked on for a ticketing tool.

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 mutation tool with no output schema, the description covers prerequisites (API key), consent requirement, side effects (email, dashboard), rate limits, error behavior, and return fields (ticket_id, ticket_ref, status). Nothing an agent needs in order to call it safely is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description adds a real constraint not present in the schema: the `body` must describe the problem and must exclude secrets, API keys, passwords or customer data. The optional `job_id`, `tool_name`, `category_slug` and `on_behalf_of_user` parameters are left to their schema descriptions.

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?

States a specific verb and resource ('Open a support ticket in your user's AlterLab account') and immediately scopes it to the case where the user needs a human. It explicitly names the sibling it is not (`alterlab_feedback`), so an agent can route between the two without inspecting schemas.

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

Usage Guidelines5/5

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

Gives explicit when-to-use ('billing, account or data problem'), when-not ('scrape friction, blocks or ideas'), the named alternative (`alterlab_feedback`), and a prerequisite behavioral rule ('Ask the user first'). This is the full when/when-not/alternative triad.

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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