flatten
Flatten a nested JSON object into dot-notation keys.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Nested object to flatten |
data-transform
Schema Changelog
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
- First observed
TDQS
A3.5/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'dot-notation keys' but omits edge cases (arrays, key collisions, circular references), whether the input is mutated, or the exact return format. This is insufficient for a transformation tool without annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words. It is front-loaded with the action and outcome, making it immediately understandable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one parameter, no output schema, no annotations), the description covers the core purpose. However, it lacks details on output structure and array handling, which would be valuable for a complete understanding. It is minimally adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% as the only parameter 'data' is described as 'Nested object to flatten'. The description repeats this concept without adding new semantics, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('flatten') and resource ('nested JSON object'), clearly stating the output format ('dot-notation keys'). It distinguishes itself from the sibling tool 'unflatten' by implying the opposite operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is self-evident, but the description provides no explicit guidance on when to use this tool versus alternatives like 'pick' or 'unflatten'. Context suggests flattening is useful for converting nested structures, but no exclusions or alternative recommendations are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Try in Browser
Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
A3.7/5.0
Disambiguation5/5
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.
Naming Consistency3/5
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.
Tool Count5/5
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.
Completeness4/5
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.