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Agent feature row (token-level, one call)

robinx_agent_features
Read-only

THE FEATURE ROW FOR A TRADING AGENT. One call returns the token-level feature row for a Robinhood Chain token, ready to fold into your own model: immutable launch facts (deployer, launch block, age); current activity (swaps, WETH volume, distinct traders, is_real, and the exact block it crossed that threshold, recovered back to chain genesis); the deployer's current track record (score, launched, real vs dead); the latest price/FDV quote with its true observation time; and observed social measurements (exact mention counts, distinct accounts, first mention, 24h velocity, how many mentioning accounts are MEASURED callers with a track record, the best early-rate among them, and what share sit in a mutual-follow cluster — i.e. one correlated voice rather than independent confirmation). The response is split by epistemic status: current_state is recomputed in place and carries NO history, so joining it to a past timestamp is lookahead bias; observed sits on an immutable capture clock. Robinhood Chain only. RobinX publishes inputs, NOT predictions — there is no score of expected return, no recommendation and no financial advice here; backtest these inputs against your own forward returns and draw your own conclusions. Costs $0.03 USDC on Base. (paid $0.03 — see instructions)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenYesRobinhood Chain token address (0x + 40 hex)

TDQS

A4.3/5.0
Behavior5/5

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

The description provides extensive behavioral details: what data is included (immutable launch facts, current activity, deployer track record, price, social measurements), explains the epistemic status of fields (current_state vs observed), and discloses costs. Annotations already indicate read-only, non-destructive behavior, and the description adds significant context beyond that.

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 quite lengthy and dense with details. While it is front-loaded with the main purpose, the extensive enumeration of fields and warnings makes it less concise than ideal. Some information could be condensed without loss of clarity.

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?

Given the single parameter and no output schema, the description fully compensates by detailing the output structure, costs, and usage caveats. It provides sufficient context for an agent to understand and invoke the tool correctly.

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?

The input schema already describes the single parameter 'token' with format '0x + 40 hex'. Schema coverage is 100%, so the description adds minimal additional semantic value beyond confirming the chain and usage context.

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 that the tool returns a 'token-level feature row' for a Robinhood Chain token, ready for model input. It distinguishes from siblings by focusing on a single token's comprehensive data for feature engineering.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this (for model input), warns about lookahead bias and no predictions, and specifies it's for Robinhood Chain only. However, it does not explicitly contrast with sibling tools like robinx_token or provide when-not-to-use guidance.

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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TDQS

A4.2/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness4/5

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.