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mouse114514

Xadeus-QQ-MCP

send_message

Send messages to QQ groups or friends with automatic reply waiting. Control message splitting via </分段> tag or punctuation-based options.

Instructions

Send a message to a monitored group or whitelisted friend. By default automatically waits for a reply after sending.

Preferred way to send multiple messages: insert </分段> in the content at each desired split point. Each segment becomes its own message; the tag itself is stripped. Example: content = "吃了吗</分段>今天忙不忙" sends two messages: "吃了吗" and "今天忙不忙". Use this whenever you want to split a reply into multiple messages — it is more natural than num_chunks because you choose the split points yourself.

Args: target: Group ID or friend QQ ID. content: Text message content. May contain </分段> markers to specify exact split points between messages. target_type: "group" (default) or "private". reply_to: Optional message ID to reply to. split_content: If True (and content has no </分段> tag), auto-split short messages (≤100 chars) on punctuation. Default False. num_chunks: Force exactly this many chunks via punctuation-based merging. Overrides the </分段> tag. Set to 1 to force a single message even when the content contains </分段>. wait_reply: If True (default), blocks and waits for a reply after sending. If False, returns immediately without waiting.

Split-point priority: num_chunks=1 → num_chunks≥2 → </分段> tag → split_content → single message.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetYes
contentYes
target_typeNogroup
reply_toNo
split_contentNo
num_chunksNo
wait_replyNo
Behavior4/5

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

Since no annotations are provided, the description bears full responsibility. It discloses that the tool blocks and waits for a reply by default, explains the splitting behavior, and the priority order. It does not mention any side effects like rate limits or permissions, but the core behavior is well-documented.

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 description is front-loaded with the primary purpose and then provides structured sections for splitting behavior and parameter details. It is appropriately detailed without being excessively long, though some repetition could be trimmed.

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?

Given the tool's complexity (7 parameters, no output schema), the description covers all necessary aspects: purpose, splitting logic, parameter explanations, and priority rules. It even includes an example. The description is self-contained and fully prepares the agent for correct invocation.

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

Parameters5/5

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

The input schema has 7 parameters with 0% description coverage, so the description must compensate. It does so thoroughly, explaining each parameter (target, content, target_type, reply_to, split_content, num_chunks, wait_reply) and providing an example. It also clarifies the split-point priority, adding significant value beyond the schema.

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 'Send a message to a monitored group or whitelisted friend', which is a specific verb+resource combination. It distinguishes itself from siblings like send_file, send_image, send_voice, and recall_message by focusing on sending text messages.

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 provides detailed guidance on splitting messages with </分段> and the split-point priority. It explains when to use split_content vs num_chunks vs the tag. However, it does not explicitly state when this tool should be avoided in favor of alternatives like send_image or send_file.

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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