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BusyBee MCP

BusyBee MCP

by danfeistar
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_comments

Fetch comments for a specific post and return unified fields for commenter, content, likes, and timestamp, enabling audience reaction analysis and follow-up insight generation.

Instructions

拉某条内容的评论列表（统一字段：评论人、内容、点赞、时间）。拿到评论后用 get_comment_insights 出结论。

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_comments"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
post_idYes
platformYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden; it does disclose that results are read-only raw comments with unified fields, which is real added value. It says nothing about pagination behavior despite a limit parameter, ordering of results, or what happens when a post has no comments.

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?

Two short sentences with zero filler: the fetch purpose and output fields come first, the follow-up routing second. Nothing to cut.

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?

It covers output fields and next-step workflow, but for a 3-parameter tool with 0% schema coverage, no annotations, and no output schema, gaps remain around platform value space, limit/pagination semantics, and error behavior for a missing post.

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% and the description only implies post_id via "某条内容"; platform and limit are never mentioned. No hint about valid platform values (a list_platforms sibling exists) or what limit controls, so the agent must guess at two of three parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific verb+resource: fetching the comment list for a given piece of content, and adds that fields are normalized (commenter, content, likes, time). That implicitly separates it from the sibling get_comment_insights, which produces conclusions rather than raw comments, though it never explicitly says it is the raw-fetch counterpart.

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?

"拿到评论后用 get_comment_insights 出结论" gives an explicit downstream alternative and the condition that selects it, which is genuinely useful routing. It stops short of stating when not to use this tool or any prerequisites (e.g. needing a valid post_id from search_posts).

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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