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BusyBee MCP

BusyBee MCP

by danfeistar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_comment_insights

Analyzes sampled post comments to summarize sentiment distribution, question ratio, frequent terms, and top-liked comments, helping answer what audiences are discussing.

Instructions

评论洞察：先拉样本评论再本地聚合，直接给结论——情绪分布、提问占比、高频词、高赞评论。比原始评论更适合回答“用户到底在说什么”。

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": {
      "platform": "<platform>",
      "post_id": "<post_id>"
    },
    "name": "get_comment_insights"
  }
}

Input Schema

NameRequiredDescriptionDefault
sampleNo
post_idYes
platformYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the burden. It usefully discloses the sample-then-aggregate-locally pipeline and the returned metrics, but omits read-only confirmation, permissions, cost, or rate-limit context for the underlying pull.

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?

Two compact sentences, front-loaded with purpose and outputs. Efficient, though the closing comparative clause could be merged tighter.

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?

Good coverage of what is returned (the main value for a tool lacking an output schema), but the incomplete parameter semantics leave an agent unsure how to supply post_id/platform correctly.

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 coverage is 0%, so the description must compensate. It only implicitly hints at the 'sample' parameter ('拉样本评论'); it says nothing about the format/meaning of post_id or platform (required params), leaving them undocumented anywhere.

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?

States a specific verb (aggregate insights) plus concrete output artifacts — sentiment distribution, question ratio, high-frequency words, top-liked comments. It explicitly contrasts with raw comments (the get_comments sibling), so an agent can differentiate without opening the schema.

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?

Clearly frames when to prefer this over raw comments ('more suitable for answering what users are actually saying'), giving comparative context. It stops short of explicit exclusions or naming the alternative by tool name, so not a 5.

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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