Analyze a Base token for honeypot risk, taxes, liquidity and ownership. Returns risk_score, recommendation, agent_action (block|warn|allow|skip), and data_quality. Cost: $0.04 USDC via x402. LIVE.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Token contract address on Base (0x...) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses the return fields (risk_score, recommendation, agent_action, data_quality), the cost ($0.04 USDC via x402), and live status. This is meaningful behavioral context beyond what the input schema provides, though it does not mention side effects or error conditions.
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 two sentences: the first lists the key analysis aspects and outputs, the second gives cost and status. Every sentence adds value, and it is front-loaded with the primary purpose.
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 covers purpose, return fields, cost, and network. It does not explain the meaning of each output field, but that is a minor gap given the straightforward nature of the tool.
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?
Schema description coverage is 100% for the only parameter (address), which already includes a pattern and description. The tool description does not add new semantic information about the parameter, so the baseline score of 3 is appropriate.
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's function: 'Analyze a Base token for honeypot risk, taxes, liquidity and ownership.' It uses a specific verb and resource, and the listed analysis dimensions distinguish it from the sibling tools (analyze_holder_clusters, get_market_data).
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 token safety checks but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. It provides context ('Analyze a Base token') but lacks direct comparative guidance.
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 purpose: market data, token safety, and holder cluster analysis. There is no overlap in functionality, making it easy for agents to select the correct tool.
All tools follow a consistent verb_noun pattern with lowercase snake_case (analyze_, check_, get_). This creates a predictable naming convention.
With only 3 tools, the server is on the lower end of typical counts, but each tool is distinct and serves a specific financial analysis function. The count is slightly thin but not inappropriate.
The server only provides three functions, with two not yet live. Significant gaps exist, such as no tool for token price, trading history, or portfolio management, making the surface incomplete for comprehensive finance analytics.