Get options candlestick data
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | End timestamp in seconds | |
| from | No | Start timestamp in seconds | |
| limit | No | Maximum number of records to return | |
| contract | Yes | Options contract name | |
| interval | No | Time interval between data points, e.g. 1m, 5m, 15m, 30m, 1h |
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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds no additional behavioral context such as required contract parameter, return format, or pagination. Since the description adds nothing beyond the annotations, the score is low.
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 one short sentence, which is concise but under-specified. It conveys the basic action but omits important context like the required contract parameter or the distinction from underlying candlesticks. It is not as empty as a single verb, but it is minimal.
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?
Given there are 5 parameters (one required) and no output schema, the description is incomplete. It fails to mention the required 'contract' parameter, the optional time range/history parameters, or what the response contains. For a market data tool, this is insufficient.
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% coverage for all 5 parameters, so the description does not need to explain them. The description provides no additional context or examples, and does not compensate for any gaps since there are none.
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 a resource 'options candlestick data', clearly indicating this returns candlestick data for options. However, it does not distinguish between options contract candlesticks and options underlying candlesticks, which are separate sibling tools, so it lacks differentiation.
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 like list_options_underlying_candlesticks or get_fx_candlesticks. No mention of prerequisites or filtering context beyond the schema.
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.