Filter a JSON array by a query object. Supports >, <, ! prefixes for comparisons.
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Array of objects to filter | |
| query | Yes | Filter criteria (e.g. {"age":">30","status":"active"}) |
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?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It mentions support for >, <, and ! prefixes, which is useful, but it does not state whether the original array is modified, what happens with non-matching elements, type coercion, or how invalid queries are handled. The return value (presumably a filtered array) is also not explicitly described.
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 two concise sentences, front-loaded with the primary action ('Filter a JSON array') followed by the key syntax detail. Every word earns its place; no fluff or redundancy.
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 the tool's simplicity and full schema coverage, the description is minimally adequate. However, it lacks information about return values, edge cases (e.g., missing fields, type mismatches), and whether the operation is read-only or mutative. No output schema exists to clarify the result, so the description should have provided more behavioral context.
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 description coverage is 100%, so the baseline is 3. The description adds the list of supported comparison prefixes, which provides some extra meaning beyond the schema example, but it largely repeats what the schema already explains (e.g., the query object example with '>30'). No significant new information is added for either parameter.
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 ('Filter') and resource ('a JSON array') with a clear mechanism ('by a query object'). This distinguishes it from sibling tools like pick (selecting properties), sort (ordering), and stats (aggregating), making the purpose unambiguous.
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 filtering JSON arrays by query criteria and gives details on comparison prefixes, but it does not explicitly state when to use this tool versus alternatives like pick or validate, nor does it mention any exclusions or prerequisites. The example in the schema provides context, but the description itself lacks explicit before/after guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.