Free live scan: this month's Reddit threads where people are complaining about a named product, asking for alternatives, or evaluating it. Classified by complaint-shaped language with the threads as receipts. Limited to 5 scans per day.
| Name | Required | Description | Default |
|---|---|---|---|
| competitor | Yes | The product or company name, e.g. "HeyReach". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and is quite transparent. It discloses that the scan is free, limited to 5 per day, covers only this month's threads, and uses complaint-shaped language classification. It doesn't mention data retention or exact output structure, but 'threads as receipts' hints at the return format.
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?
The description is exceptionally concise: three short sentences covering purpose, scope, method, and rate limit with no filler. The information is front-loaded with 'Free live scan' and every sentence contributes meaning.
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?
For a simple one-parameter tool with no output schema, the description provides sufficient context: what it scans, the time window, classification logic, and the daily limit. The only minor gap is not specifying the exact return format, but 'threads as receipts' partially covers this.
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?
The schema already fully describes the single parameter 'competitor' with an example ('HeyReach'), so the description adds no extra semantic meaning. Baseline score of 3 applies due to high schema coverage (100%).
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 scans Reddit threads for complaints, alternatives, and evaluations about a named product, with a specific time scope ('this month'). It also mentions classification by complaint-shaped language, distinguishing it from sibling tools like 'find_buyer_threads' which likely target buying signals.
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?
The description implies use for competitor complaint research but does not explicitly say when to use it over alternatives or when not to use it. Sibling tools are not referenced, so there is no comparative guidance, but the context is clear enough for an implied use case.
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.
Several tools (competitor_complaints, demand_radar, reddit_demand_board, find_buyer_threads) all mine Reddit threads for demand, complaints, or buyer interest, with overlapping purposes that could easily confuse an agent. The boundaries are unclear, especially between demand_radar and reddit_demand_board, which both offer weekly demand overviews.
All names use snake_case and are readable, but the pattern is mixed: some are verb-first (find_buyer_threads, get_demo_report, get_started) while others are noun-heavy phrases (competitor_complaints, reddit_demand_board, subreddit_rules). This inconsistency makes the set feel less predictable.
With 7 tools, the set is well-scoped for a focused Reddit market research server. Each tool serves a distinct function within the domain, and none feel redundant or excessive.
The tool set covers the core workflows: discovering demand, finding buyer threads, checking competitor complaints, and understanding community rules. Minor gaps include lack of direct keyword search or alerting, but agents can chain existing tools to achieve most goals.