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by io.github.Shree-git
OverviewDiscussionsAdmin

Draft Reply

draft_reply

Generate an AI-powered reply draft for a social mention. Returns a draft with tone and safety flags.

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": {
      "mention_id": "<mention_id>"
    },
    "name": "draft_reply"
  }
}

Input Schema

NameRequiredDescriptionDefault
toneNoReply tone: friendly, professional, casual
max_lengthNoMaximum reply length in characters (default: 280)
mention_idYesThe ID of the mention to reply to (required)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already indicate non-read-only, non-idempotent behavior, and the description adds that it returns a draft with tone and safety flags. No additional side effects, persistence, or safety details are disclosed, but the description does not contradict annotations.

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?

The description is two sentences, front-loaded with the core action and followed by the output summary. Both sentences add necessary information with no redundancy.

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

Completeness4/5

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

The description covers the tool's purpose and return value, and the schema fully documents parameters. It omits details about safety flags or persistence, but for a simple draft-generation tool, the information is adequate.

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 input schema provides descriptions for all three parameters (mention_id, tone, max_length), so the description adds no additional parameter meaning. Since schema coverage is 100%, the baseline of 3 applies.

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 tool generates an AI-powered reply draft for a social mention, distinguishing it from sibling tools that actually post replies (reply_to_comment, reply_to_conversation). It also specifies the return value (draft with tone and safety flags).

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

Usage Guidelines4/5

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

The description implies appropriate use by mentioning 'social mention' and 'draft', and the tool's name reinforces this. However, it does not explicitly exclude when to use alternatives like reply_to_comment or provide comparison guidance.

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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