Get CandyDrop activity rules including prize pools and tasks (public). Pass activity_id OR currency (at least one required by API)
| Name | Required | Description | Default |
|---|---|---|---|
| currency | No | Project/currency name; use with or instead of activity_id | |
| activity_id | No | Activity ID; use with or instead of currency |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful context by stating it is public and that the response includes prize pools and tasks, which goes beyond the structured annotations and informs the agent about response content and accessibility.
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 a single sentence that efficiently packs the tool's purpose, the key resource, and the parameter requirement. There is no filler, repetition, or unnecessary detail, making it exceptionally concise and well-structured.
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 read-only tool with only two parameters and no output schema, the description adequately covers what the tool does, what it returns, and the key invocation constraint. It could potentially detail the return structure more, but the low complexity and strong annotations make this sufficient.
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?
The input schema has 100% description coverage for both parameters. The description adds the crucial OR relationship and the fact that at least one is required, which is not explicitly stated in the individual parameter descriptions. This additional constraint is valuable for correct invocation.
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 uses a specific verb 'Get' and resource 'CandyDrop activity rules' with content details (prize pools and tasks), clearly distinguishing it from the sibling tool 'cex_launch_get_candy_drop_activity_list_v4' which lists activities. It is specific and unambiguous.
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 explicitly states the calling convention: 'Pass activity_id OR currency (at least one required by API)'. This provides clear context for parameter usage. However, it does not explicitly mention when to use this tool instead of the sibling activity list tool, so it stops short of full alternative 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.
Every tool is clearly scoped by its domain prefix (spot, fx, options, etc.) and resource type, with no two tools serving the same purpose. Even similar data types like candlesticks and order books are unambiguously separated by market.
The naming follows a strong pattern: cex_<domain>_<verb>_<resource>. However, the use of 'get' vs 'list' is occasionally inconsistent (e.g., cex_fx_get_fx_tickers vs cex_spot_list_currencies), and some names are verbose with version suffixes like 'v4'.
With 63 tools, this is an extreme count that will overwhelm an agent. While the breadth covers many product lines, the vast number of endpoints makes selection difficult and violates the typical scope for an MCP server.
The tool surface comprehensively covers public market data across spot, futures, options, delivery, earn, margin, lending, launch, and social products. Minor gaps exist (e.g., no historical trade depth, no single-ticker convenience methods), but the core data needs are fully addressed.