ETL-D MCP Server

finance_prep_v1_pipelines_finance_prep_post

Extract amounts, categorize expenses, and map accounting codes from messy transaction strings in one call.

Instructions

Orchestrates Money Extraction, Categorization, and Accounting Mapping in a single call. Ideal for processing bank statement rows or messy expense descriptions.

Cost: 3 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": {
      "raw_transaction": "<raw_transaction>"
    },
    "name": "finance_prep_v1_pipelines_finance_prep_post"
  }
}

Input Schema

Name	Required	Description
`context`	No	Optional context for currency and locale hints.
`amount_hint`	No	Optional numeric amount if already known, to help the categorizer.
`raw_transaction`	Yes	A messy transaction string (e.g., 'Uber ride to airport'). USE THIS PIPELINE for end-to-end finance processing. It extracts amounts, categorizes the expense, and maps it to accounting codes in one call.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.2.0Aug 25, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions a cost of 3 credits per request, which is useful, but does not disclose whether the operation is read-only, how errors or partial results are handled, or any rate limits. Minimal behavioral context beyond cost.

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 two sentences: the first states the primary function, the second gives use case and cost. It is front-loaded and contains no filler.

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?

The description covers the core function and a key use case, but given it is an orchestrator with no output schema and no annotations, it omits details about return format, error scenarios, or any operational constraints. Adequate but not exhaustive.

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%, so parameters are well documented. The description adds a hint that raw_transaction is typically a messy transaction string, reinforcing the schema example, but does not go beyond what the schema already provides. The baseline 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 states a specific orchestration of Money Extraction, Categorization, and Accounting Mapping in a single call, with a clear use case for bank statement rows or messy expense descriptions. This distinctly separates it from sibling tools that handle individual steps like enrich_amount or categorize_item.

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 gives a positive usage context ('Ideal for processing bank statement rows or messy expense descriptions'), but does not explicitly contrast with alternatives or advise when not to use this pipeline versus using individual endpoints. It is clear but lacks explicit exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server