Returns the full schema fragment for one advanced feature. The argument must be an exact name from get_schema().featureNames — it is a fixed enumeration, not a free-text description of what you want to build.
| Name | Required | Description | Default |
|---|---|---|---|
| feature | Yes | Feature name from get_schema().featureNames |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / feature / descriptionPrevious value: -"Feature name from get_schema().features[].name"New value: +"Feature name from get_schema().featureNames"Input schema / properties / feature / enumAdded value: +[
+ "conditionalFormats",
+ "cellTypes",
+ "filter",
+ "outlines",
+ "protection",
+ "alternateRows",
+ "view",
+ "frozen",
+ "numberFormats",
+ "richText"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral disclosure burden. It clearly indicates a read-only retrieval ('Returns') and emphasizes the critical constraint of using exact enumeration values, but does not discuss error behavior or response format. Adequate for a simple lookup tool.
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?
Two sentences deliver the essential information with zero fluff: what is returned and how to determine a valid argument. Front-loaded action and resource make it easy to scan.
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 one-parameter read-only tool with a constrained enum, the description plus schema is fully sufficient. It references the upstream get_schema tool for obtaining valid names, making invocation unambiguous. No output schema is needed for a schema-fragment lookup.
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 sole parameter has full schema coverage with an enum and description. The description adds valuable semantic layer—'must be an exact name from get_schema().featureNames' and 'fixed enumeration, not free-text'—which goes beyond the schema description and prevents misuse.
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?
Description starts with specific verb 'Returns' and object 'the full schema fragment for one advanced feature', clearly distinguishing from siblings like get_schema (which lists feature names) and get_sheet/publish_sheet. Purpose is unambiguous and well-scoped.
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?
Description explicitly instructs that the argument must be an exact name from get_schema().featureNames and is a fixed enumeration, not a free-text description. This gives clear context for when to use the tool, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.