robinx_deployerDeployer reputation rap sheet: tokens launched, real vs dead, best-token volume, insider-distribution flag, 0-100 score. (paid $0.01 — see instructions)
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Deployer wallet 0x… |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond annotations by detailing the output fields (tokens launched, real vs dead, etc.) and noting the cost ($0.01). It does not contradict annotations, and it provides additional transparency about what data is retrieved.
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 concise, packing key information into one sentence with a list of outputs. It avoids unnecessary words and front-loads the purpose. Slightly fragmented due to list format, but overall efficient.
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 the tool has one parameter, full schema coverage, and annotations, the description covers the purpose and output fields. However, the absence of an output schema means the description should provide more detail on the return format or structure, which it does not. The listing is helpful but incomplete for an agent to fully interpret the response.
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?
With 100% schema coverage, the baseline is 3. The description adds minimal parameter semantics beyond the schema, implicitly indicating the address should be a deployer wallet. It does not specify format constraints or additional context, so it meets but does not exceed expectations.
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 what the tool does: provides a deployer reputation rap sheet with specific metrics (tokens launched, real vs dead, best-token volume, insider-distribution flag, score). This distinguishes it from sibling tools like robinx_wallet (general wallet info) and robinx_token (token info), making its purpose unambiguous and differentiated.
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 usage for assessing deployer reputation through the listed fields, but does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites. The mention of paid cost is a side note, not a usage guideline.
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.