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nc-mcp-server

nc-mcp-server

by cloud-py-api
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

remove_message_reminder

DestructiveIdempotent

Cancel a reminder for a specific message by providing its conversation token and message ID, returning a confirmation.

Instructions

Cancel your reminder about a message.

Args: token: The conversation token. message_id: The message the reminder is for.

Returns: Confirmation message.

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": {
      "token": "<token>",
      "message_id": "<message_id>"
    },
    "name": "remove_message_reminder"
  }
}

Input Schema

NameRequiredDescriptionDefault
tokenYes
message_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.9.0

TDQS

B3.2/5.0
Behavior3/5

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

Annotations already declare destructiveHint=true, idempotentHint=true, and readOnlyHint=false, so the safety profile is covered. The description adds that a confirmation is returned, but does not say what happens if no reminder exists or what permissions are needed, so it adds only modest context beyond the structured data.

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?

The operation is stated in the first sentence and the parameter notes follow compactly. The 'Returns: Confirmation message' line is somewhat redundant given the output schema exists, but the overall text is short and front-loaded.

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?

For a two-parameter mutation with annotations and an output schema, the description covers purpose and both parameters adequately, and the return value is already documented by the output schema. It omits failure modes (e.g., no existing reminder) and any mention of the counterpart tools, leaving minor gaps.

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 0%, so the description carries the burden, and it does roughly identify each parameter ('the conversation token', 'the message the reminder is for'). However, it adds no format, type, or scope detail beyond those bare identifiers, so it only partially compensates for the coverage gap.

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 and resource ('Cancel your reminder about a message'), which is unambiguous and clearly distinct in name from set_message_reminder and list_message_reminders. It stops short of explicitly naming the sibling that sets a reminder, so no sibling differentiation is spelled out.

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 what the tool does but gives no guidance on when to reach for it versus set_message_reminder or list_message_reminders, and no prerequisites or conditions. An agent must infer the usage context from the name alone.

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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