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ETL-D MCP Server

ETL-D MCP Server

by pablixnieto2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

parse_xml_flatten_v1_parse_xml_flatten_post

Convert nested XML strings into flat key-value JSON for ETL pipelines. Transforms complex XML into structured, queryable data for ingestion.

Instructions

Converts complex, deeply nested XML strings into a flat Key-Value JSON structure. Ideal for data ingestion pipelines and ETL processes.

Cost: 2 credits per request.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "xml_string": "<xml_string>"
    },
    "name": "parse_xml_flatten_v1_parse_xml_flatten_post"
  }
}

Input Schema

NameRequiredDescriptionDefault
xml_stringYesThe raw XML content to flatten into Key-Value pairs.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.2.0

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must disclose behavioral traits. It does mention the cost ('Cost: 2 credits per request'), which is a usage cost, and the output format (flat JSON). However, it does not disclose behavior around invalid XML, size limits, or the exact key-flattening convention (e.g., dot notation). This is a minimal but not comprehensive disclosure.

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 concise and front-loaded: it opens with the core transformation, then gives a usage context, then the cost note. Every sentence serves a purpose with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description provides the essential information: purpose, output structure, and ideal usage. However, it omits details on how keys are flattened (e.g., dot vs underscore notation) and error handling for malformed XML, which could matter to an agent deciding on downstream parsing. Still, it is largely complete for a simple transformation tool.

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?

The schema already fully describes the single parameter xml_string with 'The raw XML content to flatten into Key-Value pairs.' With 100% schema coverage, the description adds no additional semantic value beyond what the schema provides. Baseline score of 3 is appropriate.

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 explicitly states 'Converts complex, deeply nested XML strings into a flat Key-Value JSON structure', which names the specific verb (converts), resource (XML strings), and output (flat Key-Value JSON). It clearly distinguishes itself from sibling tools like parse_edi or parse_bank_statement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: 'Ideal for data ingestion pipelines and ETL processes.' This implies when to use the tool. While it does not explicitly list alternatives or when-not-to-use conditions, this is adequate given the unambiguous purpose and the sibling context.

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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