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oines

astral-bridge

by oines

qq_search_messages

Search stored QQ text messages in group or private conversations by keyword to retrieve matching conversation history.

Instructions

Search stored QQ text messages in a group or private conversation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
target_typeYes
target_idYes
queryYes
limitNo
Behavior2/5

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

With no annotations provided, the description carries full burden for disclosing behavioral traits. It only states the basic purpose and fails to describe important behaviors such as search algorithm (e.g., exact match, partial match), case sensitivity, handling of no results, authentication requirements, or pagination. This leaves significant uncertainty for an agent.

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 a single sentence of 10 words, extremely concise and front-loaded with the key action and scope. Every word is necessary; there is no superfluous information. It is the model of conciseness.

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

Completeness2/5

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

Given the tool has 4 parameters (3 required), no output schema, and no annotations, the description is too terse. It omits critical context such as the format of search results (list of message IDs? full message objects?), error behavior, rate limits, and how the search interacts with QQ's storage (e.g., date range? all history?). Sibling tools like 'qq_get_message' suggest there might be message IDs, but without output schema or further detail, an agent cannot reliably interpret results.

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%, so the description must compensate. It does not explain the semantics of individual parameters: target_id format (e.g., numeric ID or string?), query syntax (free text, exact match?), or limit behavior. It only implies via the description that target_type and target_id specify the conversation, which is already clear from the schema. This adds minimal 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 the tool searches stored QQ text messages and specifies the scope (group or private conversation). The name 'qq_search_messages' aligns with the action, and it is distinguishable from sibling tools like 'get_message' or 'get_recent_messages' which retrieve specific messages or lists rather than performing a search.

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?

No guidance is provided on when to use this tool vs alternatives. There is no mention of prerequisites, limitations, or when not to use it. The description lacks contextual cues that help an agent decide to invoke this over other message retrieval tools.

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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