Scan a wallet for unclaimed DeFi bounties, airdrops, claimable rewards, and yield opportunities across 36+ protocols on 5 chains (Ethereum, Base, Arbitrum, Optimism, Polygon). Returns a list of claimable positions with estimated USDC value. Free, no auth required.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes | Wallet address to scan for unclaimed bounties and rewards (0x... EVM address) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that the tool is free, requires no authentication, and returns estimated USDC values. Minor details like rate limits or data freshness are omitted, but for a simple scan tool this is sufficient.
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?
Two sentences with no wasted words. The action verb 'scan' is front-loaded, followed by scope (36+ protocols, 5 chains) and output. Efficient and clear.
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?
The tool has low complexity (one required parameter). No output schema, but the description specifies the return type (list with USDC value). It is complete enough for an agent to understand the tool's capability and output format.
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 coverage is 100% and describes 'address' as an EVM wallet address. The description adds context about scanning for bounties/rewards but does not provide additional parameter details beyond the schema. Baseline 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 scans a wallet for unclaimed DeFi bounties, airdrops, claims, and yields across 36+ protocols on 5 chains, returning a list with USDC value. This distinguishes it from siblings like 'contract_scan' or 'tokendiet_analyze' which have different focuses.
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 provides clear context (wallet address, free, no auth) and implies usage for finding unclaimed rewards. It does not explicitly exclude scenarios or mention alternatives, but the sibling list is large and varied, making implicit differentiation acceptable.
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.
Many tools have distinct purposes, but there are several overlapping or redundant tools (e.g., leadsignal vs leadsignal_generate, multiple code audit tools, multiple trading proposal/journal tools, and several 'universal' entry points like zambo_help, zambo_ask, zambo_universal). Descriptions help, but the volume creates ambiguity.
Naming conventions vary across prefixes (zambo_, zambot_, axis_, presence_, trading_, etc.), with some tools using single words (weather, translate) and others using verb_noun patterns. Aliases like leadsignal_generate for leadsignal break consistency. While prefixes provide some grouping, the overall pattern is mixed.
125 tools is excessive for a single MCP server, even if the server aims to be a universal stack. This makes it overwhelming for agents to navigate and increases the likelihood of misselection. Many tools could be split into domain-specific servers.
The tool surface is extraordinarily comprehensive, covering agent identity, cross-layer orchestration, code analysis, content generation, legal scanning, lead generation, trading, on-chain data, and more. Nearly any common agent task is supported with multiple tools, leaving few obvious gaps.