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Copilot Desktop MCP

Copilot Desktop MCP

by avianhermana
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Notify User

notify_user

Displays a native Windows toast notification in the bottom-right corner with a title and message. Use it to alert users to timely desktop updates from AI workflows.

Instructions

Display a native Windows Toast Notification popup in the bottom-right corner of the desktop.

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": {
      "title": "<title>",
      "message": "<message>"
    },
    "name": "notify_user"
  }
}

Input Schema

NameRequiredDescriptionDefault
titleYes
messageYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.1/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full load. It does disclose genuine behavioral context the schema cannot: the notification is a native Windows toast, appears in the bottom-right corner, and is a popup rather than an in-app message. It omits whether the call blocks, whether the toast auto-dismisses, and what happens on non-Windows platforms or under Focus Assist.

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?

A single well-formed sentence with the verb and outcome front-loaded and no filler. Nothing can be trimmed without losing information.

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?

An output schema exists, so return values need not be explained, and two obvious string parameters keep the surface small. Still, with zero annotations and zero schema descriptions, an agent is not told about failure modes, platform constraints, or parameter behavior, leaving the definition only marginally complete for its complexity.

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

Parameters2/5

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

Schema description coverage is 0%, and the description never mentions the two required parameters, so it adds no meaning beyond the raw field names title and message. The names are largely self-explanatory, which limits the damage, but length limits, formatting, or override behavior for the message body go unaddressed.

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 verb ("Display") and a concrete resource ("native Windows Toast Notification popup"), plus its on-screen placement. It is immediately distinguishable from unrelated siblings such as create_pdf or dns_lookup, though no sibling comparison is explicitly made.

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

Usage Guidelines2/5

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

The description says only what the tool does; it gives no guidance on when to prefer a toast notification over other user-facing output, no conditions or prerequisites, and no exclusions (e.g., that it should not be used for blocking prompts). The when-to-use is left entirely to inference.

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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