cex_options_list_options_underlying_tickers
Get ticker data for all contracts under an underlying
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| underlying | Yes | Underlying asset name, e.g. BTC_USDT |
Get ticker data for all contracts under an underlying
| Name | Required | Description | Default |
|---|---|---|---|
| underlying | Yes | Underlying asset name, e.g. BTC_USDT |
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?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the read-only and safe nature is known. The description adds no extra behavioral context beyond the read-only operation, such as return format or pagination. No contradiction with annotations.
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?
A single front-loaded sentence delivers the core purpose with zero filler. It is appropriately concise for a tool with one parameter.
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 tool with one parameter and no output schema, the description provides the essential purpose but does not explain the returned ticker data structure or any limitations (e.g., pagination, market data update frequency). However, given the simplicity, it is adequate and not under-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?
The schema has 100% coverage: the only parameter 'underlying' is described with an example ('BTC_USDT'). The description's phrase 'under an underlying' merely restates the parameter name without adding new meaning. Since the schema fully documents the parameter, the description adds no extra semantic value.
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 specific language: 'Get ticker data for all contracts under an underlying.' It clearly specifies the resource (ticker data) and scope (all contracts under a specific underlying), distinguishing it from generic tools like cex_options_list_options_tickers that likely operate across all underlyings.
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: it is for fetching ticker data for a given underlying. However, it does not explicitly state when to use this over alternatives, nor does it mention any exclusions. The existence of sibling tools like cex_options_list_options_tickers and cex_options_list_options_contracts suggests possible confusion, but the underlying scoping is implied.
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.