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Datapoint MCP

Datapoint MCP

by impel-intelligence
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

add_credits

Generate a checkout URL to buy AI credits. Complete payment in your browser to add credits to your account.

Instructions

Open a checkout link to purchase Datapoint AI credits.

Returns a hosted checkout URL. The user completes payment in their browser; credits land on their account once payment confirms.

Args: product_id: Optional product identifier. Omit to use the default credit bundle configured on the server.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "add_credits"
  }
}

Input Schema

NameRequiredDescriptionDefault
product_idNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool initiates a checkout flow, requires user browser interaction, and credits are added asynchronously. It does not mention rate limits, auth needs, or failure scenarios, but overall provides sufficient behavioral context.

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?

Very concise: three sentences plus Args line. Front-loaded with main action. Clear structure with separate paragraph for return behavior and parameter.

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 simple tool with one optional param and an output schema (hosted checkout URL), the description covers the essential purpose and flow. It doesn't mention prerequisites or server configuration defaults, but overall complete enough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description explains the only parameter product_id: optional, omit for default bundle. This adds meaning beyond the schema's 'Product Id' title. However, it could provide more detail on product_id format or validation.

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?

Clearly states the tool opens a checkout link to purchase credits, and specifies it returns a hosted URL. This differentiates it from all sibling tools, none of which involve purchasing credits.

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?

Explicitly describes when to use: to purchase Datapoint AI credits. Explains the flow (user completes payment in browser, credits land after confirmation). No explicit when-not or alternatives, but given sibling set, it's clear this is the only credit purchase tool.

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

Deploy Server

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