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yigitkonur

Research Powerpack MCP

by yigitkonur

get_reddit_post

Fetch Reddit posts with smart comment allocation to extract community consensus and diverse perspectives across multiple discussions.

Instructions

🔥 FETCH REDDIT POSTS - 2-50 URLs, RECOMMENDED 10-20+

This tool fetches Reddit posts with smart comment allocation. Using 2-5 posts = missing community consensus. Use 10-20+ for broad perspective.

Comment Budget: 1,000 total comments distributed automatically across posts.

  • 2 posts: ~500 comments/post (deep dive)

  • 10 posts: ~100 comments/post (balanced)

  • 20 posts: ~50 comments/post (RECOMMENDED: broad)

  • 50 posts: ~20 comments/post (max coverage)

Comment allocation is AUTOMATIC - you don't need to calculate!

When to use different post counts:

2-5 posts: Deep dive on specific discussions

  • Use when: You found THE perfect thread and want all comments

  • Trade-off: Deep but narrow perspective

10-15 posts: Balanced depth + breadth (GOOD)

  • Use when: Want good comment depth across multiple discussions

  • Trade-off: Good balance of depth and coverage

20-30 posts: Broad community perspective (RECOMMENDED)

  • Use when: Want to see consensus across many discussions

  • Trade-off: Less comments per post but more diverse opinions

40-50 posts: Maximum coverage

  • Use when: Researching controversial topic, need all perspectives

  • Trade-off: Fewer comments per post but comprehensive coverage

Example: ❌ BAD: {"urls": ["single_url"]} → 1 perspective, could be biased/outdated ✅ GOOD: {"urls": [20 URLs from diverse subreddits: programming, webdev, node, golang, devops, etc.]} → comprehensive community perspective

Pro Tips:

  1. Use 10-20+ posts - More posts = broader community perspective

  2. Mix subreddits - Different communities have different expertise and perspectives

  3. Include various discussion types - Best practices, comparisons, problems, solutions

  4. Let comment allocation auto-adjust - Don't override max_comments unless needed

  5. Use after search_reddit - Get URLs from search, then fetch full content here

CRITICAL: Comments often contain the BEST insights, solutions, and real-world experiences. Always set fetch_comments=true unless you only need post titles.

Workflow: search_reddit (find posts) → get_reddit_post (fetch full content + comments)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYes**2-50 Reddit post URLs** (Minimum 2, recommended 10-20) More posts = broader community perspective and better consensus detection. Get URLs from search_reddit results, then fetch full content here.
use_llmNo**Enable AI-powered content extraction (RECOMMENDED: true)** When enabled, processes Reddit content through LLM to: - Extract key insights and community consensus - Synthesize opinions across multiple posts - Identify common recommendations and debates - Filter noise and surface actionable information Cost: pennies (~$0.01 per batch) Requires: OPENROUTER_API_KEY environment variable
max_commentsNo**Override automatic comment allocation** Leave empty for smart allocation based on post count: - 2 posts: ~500 comments/post - 10 posts: ~100 comments/post - 20 posts: ~50 comments/post Only override if you need specific comment depth.
fetch_commentsNo**Fetch comments from posts (RECOMMENDED: true)** Comments often contain the BEST insights, solutions, and real-world experiences. Set to true (default): Get post + comments Set to false: Get post content only (faster but misses insights)
what_to_extractNo**Extraction instructions for AI (used when use_llm=true)** Tell the AI what specific information to extract: - "Extract recommendations for [topic] with pros/cons" - "Summarize common issues and solutions mentioned" - "Identify consensus on [specific question]" If not provided, defaults to general insight extraction. More specific instructions = better extraction quality!
Behavior4/5

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

The description discloses the comment budget (1000 total), automatic allocation formula, and effects of parameters like max_comments and use_llm. It mentions cost and requirement for use_llm and recommends fetch_comments=true. However, it does not address rate limits, error handling, authentication, or how input validation works (e.g., invalid URLs). Since no annotations exist, the description carries the burden, but it is still largely transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with bold headings, bullet points, and an example section, making it scannable. However, it is quite long (multiple paragraphs, pro tips, trade-off tables). While every sentence adds value, the overall length could be trimmed for conciseness without losing essential information.

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?

Given the 5 parameters and no output schema, the description thoroughly explains input behavior and workflow. However, it does not describe the output structure or return format (e.g., what fields the response contains). An agent would need to infer or assume the output shape. This is a notable gap, especially with no output schema provided.

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

Parameters4/5

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

Schema description coverage is 100%. The description adds value beyond the schema by explaining the automatic calculation for max_comments, cost and environment variable for use_llm, and extraction instructions for what_to_extract. It also provides examples for what_to_extract and pro tips for fetch_comments. This enriches the agent's understanding without redundancy.

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 it fetches Reddit posts with smart comment allocation, specifying the resource (Reddit posts) and action (fetch). It distinguishes itself from sibling tools like search_reddit, which finds posts, and deep_research, web_search, scrape_links, which serve different purposes.

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 description provides explicit guidance on when to use different post counts (2-5 for deep dive, 10-15 balanced, 20-30 recommended, 40-50 controversial), trade-offs, and workflow linking to search_reddit. It also gives a concrete example of a bad vs good URL array, and pro tips for subreddit mixing.

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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