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mouse114514

Xadeus-QQ-MCP

batch_get_recent_context

Batch query recent message context from multiple group or private targets. Define targets with optional type and message limit to reduce API calls.

Instructions

Batch query recent message context for multiple targets.

More efficient than calling get_recent_context multiple times: uses at most 2 OneBot API calls (group list + friend list) regardless of how many targets are queried.

Args: targets: List of dicts, each with "target" (ID) and optional "target_type" ("group" or "private", default "group"). Example: [{"target": "123", "target_type": "group"}, {"target": "456", "target_type": "private"}] limit: Number of recent messages per target (default 50).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetsYes
limitNo
Behavior4/5

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

With no annotations, the description carries the full burden. It explains the tool uses at most 2 API calls regardless of target count, and lists parameters. It does not explicitly state non-destructiveness, but as a query tool, the behavior is clear and well-described.

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 well-structured: a concise purpose line, followed by efficiency note, then parameter details. It is informative but not overly verbose, earning its length.

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?

Given no output schema, the description explains what is queried (recent message context) and the parameters, which is sufficient for a batch query tool. It could mention return format or error handling, but overall complete enough for its complexity.

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?

Schema description coverage is 0%, so the description must explain parameters. It thoroughly describes 'targets' as a list of dicts with optional 'target_type' and provides an example, and explains 'limit' as number of recent messages per target. This adds full meaning beyond the bare 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 'Batch query recent message context for multiple targets,' specifying the verb and resource. It distinguishes itself from the sibling get_recent_context by highlighting the batch nature and efficiency over multiple calls.

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 explicitly states it is 'More efficient than calling get_recent_context multiple times' and provides API call optimization details, guiding when to use this tool instead of the single-target alternative. No exclusions are mentioned, but the context is clear.

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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