Get contract statistics (open interest， long/short ratio， etc.)
| Name | Required | Description | Default |
|---|---|---|---|
| from | No | Start timestamp in seconds | |
| limit | No | Maximum number of data points to return | |
| settle | No | Settlement currency | |
| contract | Yes | Futures contract name | |
| interval | No | Time interval for data points |
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 the tool as read-only, non-destructive, idempotent, and open-world, so the safety profile is well established. The description adds examples of the statistics returned but does not disclose behaviors like pagination, rate limits, or response format, which is acceptable given the 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?
The description is a single sentence with no wasted words, front-loaded with the core purpose and enriched by concrete examples. It is appropriately concise for a simple data retrieval tool.
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 straightforward read-only list operation with comprehensive parameter descriptions and strong annotations, the description is adequate. The 'etc.' is vague, but the combination of schema and name implies a time-series list of stats, making the overall context reasonably complete.
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% (all five parameters have descriptions in the schema), so the description does not need to explain parameters. It adds no additional parameter semantics beyond what the schema already provides, hence the baseline score.
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 ('contract statistics') with concrete examples ('open interest, long/short ratio'), clearly distinguishing it from sibling tools like cex_fx_get_fx_tickers or cex_fx_get_fx_contract.
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?
There is no guidance on when to use this tool versus alternatives, no mention of exclusions or prerequisites. The description simply states what the tool does without any selection criteria.
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.