Metadata MCP Connector

Get AI Credit Transactions

get_credit_transactions

Read-only

Get recent AI credit movements for the account this token belongs to, newest first.

                USAGE INSTRUCTIONS FOR LLM:
                Use this to explain WHERE credit went, or to show that a purchase was recorded.
                Each row is one movement: a negative `amountUsd` is a charge, a positive one is
                a top-up or an adjustment. `source` says what kind of usage it was (for example
                `direct_mcp` for tool calls made from an external client like this one,
                `image_generation`, `opik_ai_chat`, `maxio_topup` for a purchase).

                WHEN TO USE THIS TOOL:
                - User asks "what have I been charged for?" or "where did my credits go?"
                - User disputes a charge and wants the detail behind it
                - After a payment, to show the customer the top-up on their history
                - To find the `referenceId` of a purchase so you can pass it to
                  `get_payment_status`

                EXPECTED RESPONSE FORMAT:
                {
                  "account_id": "1234",
                  "transactions": [
                    {"amountUsd": -0.04, "source": "direct_mcp", "description": "MCP tool: ...",
                     "referenceId": "...", "createdAt": "...", "markup": "4.000000"}
                  ],
                  "total_elements": 210, "total_pages": 9, "page": 0
                }

Input Schema

TableJSON Schema

Name	Required	Description	Default
`page`	No	Zero-based page number. Defaults to 0 (most recent).
`size`	No	Rows per page, 1-100. Defaults to 25.

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 16, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond that: it explains the sign convention (negative for charges, positive for top-ups), what 'source' values mean (e.g., direct_mcp, image_generation, maxio_topup), and provides an expected response format with fields like referenceId and pagination info. This is rich, useful behavioral disclosure that goes far beyond the structured 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 well-structured with clear sections (purpose, usage instructions, when-to-use, expected response format). It front-loads the core purpose and then provides actionable details. Every sentence earns its place, offering concrete guidance without redundancy. The formatting makes it easy for an agent to parse and apply.

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

Completeness5/5

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

Given the tool's simplicity (2 optional params, no output schema), the description is fully sufficient. It provides the expected response format explicitly, including fields and pagination details, which compensates for the lack of an output schema. It also covers the token context and interpretation of results. An agent has everything needed to call this tool correctly and interpret the response.

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 input schema covers both parameters (page and size) with descriptions, and the coverage is 100%. The description itself does not add extra meaning to these parameters beyond what the schema already states, such as default values and ranges. The description does hint at pagination via the response format (total_pages), but that is output behavior, not parameter semantics. Baseline 3 is appropriate given full schema coverage.

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 clearly states the specific verb and resource: 'Get recent AI credit movements for the account this token belongs to, newest first.' It also explains the meaning of each row (a movement with negative amountUsd being a charge and positive being a top-up), which distinguishes it from sibling tools like get_credit_balance and get_payment_status. The purpose is unambiguous and differentiated.

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

Usage Guidelines5/5

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

The description explicitly lists 'WHEN TO USE THIS TOOL' with concrete scenarios such as explaining charges, showing top-ups after payment, and finding referenceId for get_payment_status. It also provides 'USAGE INSTRUCTIONS FOR LLM' that clarify how to interpret the data. This leaves no doubt about when to select this tool over alternatives.

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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