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Token on-chain vitals

robinx_token
Read-only

On-chain stats for a Robinhood Chain token: swaps, WETH volume, unique traders, real-activity flag, deployer score. (paid $0.01 — see instructions)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesToken contract address 0x…

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the description adds the cost detail and a reference to instructions. It does not disclose further behavioral traits like rate limits, authentication needs, or return format, making it only marginally additive.

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

Conciseness5/5

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

The description is a single sentence that efficiently lists the output contents, followed by a brief cost note. Every word is purposeful, with no redundancy or fluff.

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?

With no output schema, the description should ideally explain the return structure. While it lists data fields, it does not confirm whether the response is a single object or requires pagination. Given the low parameter complexity, the description is adequate but not fully complete.

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?

Schema coverage is 100%, so the baseline is 3. The parameter 'address' is described in the schema as 'Token contract address 0x…', which is sufficient. The tool description lists the output data but does not add new parameter semantics beyond the schema.

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 defines the tool's purpose: providing on-chain stats for a Robinhood Chain token, listing specific data points like swaps, WETH volume, unique traders, real-activity flag, and deployer score. It distinguishes from sibling tools like robinx_signals or robinx_stats by focusing on token vitals.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives among the many sibling tools. The phrase 'paid $0.01 — see instructions' hints at a cost consideration but provides no direct guidance on selection criteria or exclusions.

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.