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by io.github.trycourier
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get_user_preference_topic

Read-only

Get one user's choice for a single subscription topic — whether they opted in or out, and per channel. Use this when you already know which topic is in question; get_user_preferences returns all of them at once.

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": {
      "user_id": "<user_id>",
      "topic_id": "<topic_id>"
    },
    "name": "get_user_preference_topic"
  }
}

Input Schema

NameRequiredDescriptionDefault
user_idYesThe user ID
topic_idYesThe subscription topic ID
tenant_idNoOmit this for the user's workspace-level choice, which answers most questions. Only pass it when you need this topic as scoped to one specific tenant.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / tenant_id / description
      Previous value: -"Scope to a specific tenant"New value: +"Omit this for the user's workspace-level choice, which answers most questions. Only pass it when you need this topic as scoped to one specific tenant."
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description reinforces this with 'Get'. It adds value beyond the annotation by disclosing the return content ('whether they opted in or out, and per channel'), which helps the agent understand what to expect. No contradiction or missing critical behavioral detail.

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 crisp sentences. The core function is stated first, followed by the selection guidance and sibling comparison. No wasted words.

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 simple read-only retrieval tool with a fully documented schema and annotations, the description is complete: it states what it returns, when to use it, and how it differs from the sibling. The lack of an output schema is not a gap because the description communicates the key output fields.

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?

The schema description coverage is 100%, so the schema already fully documents all three parameters. The description does not add meaning beyond what the schema provides for parameters, though it clarifies the output context. Baseline 3 is appropriate.

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?

The description clearly states the specific verb and resource: getting a single user's choice for a single subscription topic, including opt-in/out status per channel. It explicitly differentiates this from the sibling tool get_user_preferences, which returns all topics at once.

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?

The description provides explicit guidance on when to use this tool: when you already know which topic is in question, and contrasts it with get_user_preferences as the alternative for retrieving all topics. This leaves no ambiguity about selection.

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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