robinx_social_momentumRobinhood Chain tokens ranked by measured caller-accuracy weight, then independent voices, then mention count — mutual-follow clusters collapsed to ONE voice each. Attention QUALITY, not raw hype counts. Window 1-48h (default 6). Costs $0.02 USDC on Base. (paid $0.02 — see instructions)
| Name | Required | Description | Default |
|---|---|---|---|
| hours | No | lookback window in hours, 1-48 (default 6) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds substantial behavioral info: the ranking algorithm (caller-accuracy weight, independent voices, mutual-follow cluster collapse), the time window, and the cost ($0.02 USDC on Base). This exceeds what annotations provide.
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 compact but not overly terse, covering algorithm, time window, cost, and quality emphasis in three sentences. It is front-loaded with the ranking method and avoids unnecessary detail.
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?
No output schema exists, so the description should explain the return format (e.g., list of tokens with scores). It only says 'tokens ranked,' leaving agents to guess the output structure. Given the algorithm's complexity, this is a significant gap.
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 only parameter 'hours' has full schema coverage (100%) with a clear description. The description adds context (default 6, range 1-48) and explains that it's the lookback window for the ranking, adding value beyond the schema's minimalist description.
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 ranks tokens by a specific methodology (caller-accuracy weight, independent voices, mention count) and emphasizes attention quality over raw hype. It distinguishes itself from simple mention counters by specifying the de-hyped ranking algorithm.
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 quality-adjusted social momentum by contrasting with 'raw hype counts,' but it does not explicitly mention when to use this tool over siblings like robinx_mentions or robinx_signals. The cost and time window are noted, but usage context is only implicit.
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.
Each tool targets a distinct aspect of Robinhood Chain token analysis: deployer reputation, on-chain stats, social sentiment, wallet history, etc. Even overlapping concepts like verdict and report are clearly differentiated by scope and detail.
All tools follow a consistent 'robinx_<single_word_noun>' pattern, making it easy for an LLM to predict tool names and understand their purposes at a glance.
12 tools is optimal for a domain-specific server covering deployer analysis, token stats, social signals, wallet reputation, and composite reports. The count feels neither sparse nor bloated.
The tool set covers the core token analysis workflow: search, deployer check, on-chain stats, holder structure, social pulse, and comprehensive reports. Minor gaps like batch operations or historical comparisons exist but do not severely hinder usability.