List delivery tickers.
| Name | Required | Description | Default |
|---|---|---|---|
| settle | No | Settlement currency for delivery (e.g. usdt, btc) | |
| contract | No | Optional delivery contract identifier; if set, only that contract's ticker is returned |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral context beyond what the name implies. Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered, but the description does not mention filtering, pagination, or response details. The optional contract parameter's behavior is only documented in the schema, not in the description.
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, front-loaded sentence with zero wasted words. It is appropriately sized for a simple list operation.
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 is simple, and the annotations plus schema provide adequate context. However, the description does not clarify what a 'delivery ticker' is, nor does it mention any output format or filtering behavior beyond the schema parameter notes. Given the low complexity and rich annotations, a minimal description is acceptable, but it remains thinly specified.
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%, with both 'settle' and 'contract' clearly explained. The tool description itself adds no parameter-level detail, so the baseline score of 3 is appropriate when the schema carries the semantic weight.
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 'List delivery tickers' uses a specific verb and resource, clearly distinguishing this tool from siblings like cex_dc_list_dc_contracts and cex_spot_get_spot_tickers. It precisely names the domain (delivery) and the action (list tickers).
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 no guidance on when to use this tool versus alternative ticker-listing tools, nor does it mention any exclusions or prerequisites. It only states the basic function.
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.