Scrape Reddit posts and comments by subreddit, keyword search, or post URL. Returns full post data with comment trees.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Scrape mode | |
| query | No | Search query (for search mode) | |
| postUrl | No | Reddit post URL (for post mode) | |
| username | No | Reddit username (for user mode) | |
| subreddit | No | Subreddit name without r/ prefix (for subreddit mode) | |
| maxResults | No | Max posts (default 25) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only states it returns 'full post data with comment trees', but omits behavioral details like rate limits, authentication requirements, error handling, or whether it respects Reddit's API policies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise, front-loaded sentences. Every word adds value. No redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complexity (6 params, 4 modes) and no output schema, the description is minimally adequate. It states return type but lacks guidance on usage context, error scenarios, or edge cases. Could benefit from mentioning output structure or limitations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific information beyond what the schema already provides. The enum for mode is described in schema, and description only restates some modes.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: scrape Reddit posts and comments by various modes. It specifies the verb ('scrape'), resource ('Reddit posts and comments'), and methods ('subreddit, keyword search, or post URL'), distinguishing it from sibling tools which focus on other platforms.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., when to use scrape_reddit vs search or other scraping tools). The different modes are implied but not explained in terms of use cases or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target unique data sources or specific actions (e.g., search_zillow vs. get_zillow_property_details are clearly sequential). A few LinkedIn-related tools (find_linkedin_candidates vs. search_linkedin_employees) have overlapping purposes but their descriptions clarify distinct use cases. Overall, confusion is minimal and descriptions resolve ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, using verbs like search, get, find, scrape, analyze, lookup, resolve, and verify. The pattern is predictable across the entire set, making it easy for an agent to infer function from name.
With 32 tools, the server exceeds the 'too many' threshold of 25+. While the broad scope of web data mining justifies some diversity, the count is unwieldy and could overwhelm an agent's selection process. A smaller, more focused set per domain would improve coherence.
The toolset covers a wide range of data retrieval needs: company research, real estate, job listings, academic research, and government records. For a read-only data aggregation service, there are no major lifecycle gaps, though some subdomains like social media scraping only cover Reddit and LinkedIn, missing other platforms. Overall, it is reasonably complete for its stated purpose.
