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hn-mcp

hn-mcp

by tomwojcik
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_comment_tree

Retrieve a specific Hacker News comment and its reply subtree, allowing you to drill into discussion branches with configurable depth.

Instructions

Fetch a specific comment and its reply subtree.

Use this to dive into a branch after scanning top-level comments
with get_thread(depth=1).

Args:
    comment_id: The HN comment ID.
    depth: How many levels of replies to include below this comment.
        0 = this comment only (with reply_count).
        N = N levels of replies.
        -1 = full subtree (default).

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": {
      "comment_id": "<comment_id>"
    },
    "name": "get_comment_tree"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNo
comment_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains depth semantics: 0 means only the comment with reply_count, N means N levels, -1 means full subtree. This goes beyond schema defaults and gives the agent essential knowledge about response behavior.

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 compact and front-loaded: a one-line summary, a usage sentence, then a structured Args list. Every element serves a purpose, with no redundant fluff. The format is easy to scan and parse.

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?

The tool has an output schema, so return structure is available elsewhere. The description covers the critical contextual points: how to use it relative to get_thread, how the depth parameter behaves, and what depth=0 returns. For a relatively simple two-parameter tool, this is complete and actionable.

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 coverage is 0%, meaning no parameter descriptions exist in the schema. The description compensates fully: comment_id is identified as 'The HN comment ID' and depth is detailed with all three modes (0, N, -1) and the default behavior. This adds significant meaning beyond the raw 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 opens with a specific verb and resource: 'Fetch a specific comment and its reply subtree.' This clearly distinguishes it from sibling tools like get_thread (top-level comments) and search_comments. The purpose is unambiguous and immediately understood.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The second sentence explicitly states when to use this tool: 'dive into a branch after scanning top-level comments with get_thread(depth=1).' It names the alternative tool and provides a concrete use case, giving the agent clear decision guidance.

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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