sort
Sort a JSON array by a field.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of objects to sort | |
| field | Yes | Field name to sort by | |
| order | No | Sort order (default: asc) |
Sort a JSON array by a field.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of objects to sort | |
| field | Yes | Field name to sort by | |
| order | No | Sort order (default: asc) |
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?
With no annotations, the description carries the full burden, but it only states the basic operation. It does not disclose whether the sort is stable, whether the input array is mutated, how missing fields are handled, or the nature of the return value.
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, focused sentence that is front-loaded with the verb 'Sort' and directly states the target. Every word earns its place, and there is no unnecessary elaboration.
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 simple sort tool, the description is adequate for the core action but lacks details about the return value, mutation behavior, or error cases. With no annotations or output schema, this leaves notable gaps for an agent to predict behavior fully.
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 already provides complete descriptions for data, field, and order, including an enum for order. The tool description adds minimal semantic value beyond echoing the 'field' parameter, so the schema carries the 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 clearly states the tool sorts a JSON array by a field, using a specific verb and resource. It is easy to distinguish from sibling tools like filter or pick, as sorting is a distinct operation.
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 for sorting but provides no explicit guidance on when to choose this tool over alternatives, nor does it mention limitations or exclusions. No context is given for edge cases or integration with other tools.
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.
Each tool has a clearly distinct purpose: conversions between formats (CSV, JSON, XML) are separate from data operations (filter, pick, sort, stats) and structural utilities (flatten, unflatten, validate). No two tools overlap in function.
The conversion tools follow a consistent 'from_to' pattern (csv_to_json, json_to_csv, json_to_xml), but the operation tools use bare verbs (filter, pick, sort) or nouns (stats), and flatten/unflatten use a different style. The mix is readable but not uniform.
With exactly 10 tools, the server covers the core data transformation needs without bloat. Each tool serves a clear purpose and the count is well within the typical ideal range of 3-15.
The toolkit covers common conversion and data manipulation tasks well. A minor gap is the lack of an XML-to-JSON converter, but conversions can be chained through JSON, and the basic transformation lifecycle is complete.