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malkreide

hn-tech-signal-mcp

by malkreide

hn_discussion

Read-onlyIdempotent

Retrieve HackerNews comment threads with full reply hierarchy to surface counter-arguments and practitioner caveats. Use story IDs from top stories or search.

Instructions

Read the comment thread under a HackerNews story.

Where hn_top_stories and hn_search tell you what is being discussed, this tells you what is actually being argued — the counter-arguments, the practitioner caveats, the "we tried this in production" replies that carry the real signal. Algolia can search comment text but does not return thread structure, so this is the only way to see who replied to whom.

Get a story_id from hn_top_stories or hn_search first.

Comments are walked breadth-first, so the highest-ranked top-level comments come back first. Deleted and flagged comments are skipped. Popular threads run to several hundred comments and each one costs a request upstream, so both depth and total count are capped — check the 'truncated' flag to see whether the thread was cut short.

Args: params (HnDiscussionInput): - story_id (int): HackerNews item ID - max_depth (int): Reply nesting levels (1–4, default 2) - max_comments (int): Total comment budget (1–100, default 25) - text_chars (int): Per-comment text truncation (100–2000)

Returns: str: JSON with story{}, total_comments (as reported by HN), fetched_comments, truncated, comments[]. Each comment: id, by, posted, text, reply_count, replies[] (same shape, nested).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond annotations (readOnly, idempotent), description details breadth-first walking, skipped comments, capping with truncated flag, and per-request cost.

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?

Well-structured with purpose, rationale, usage, behavior, and parameter list; every sentence is substantive and no waste.

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?

Covers output format, truncated flag, and capping logic; with output schema present, description provides sufficient context for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Description lists all parameters with types and ranges, but essentially duplicates the input schema descriptions; adds no new meaning.

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?

Specifically states it reads HN comment thread structure, and distinguishes from siblings hn_top_stories (what's discussed) and hn_search (Algolia search without thread structure).

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?

Explicitly instructs to get a story_id from hn_top_stories or hn_search first, and explains when this tool is the only way to see thread structure versus alternatives.

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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