Pulse MCP Server

get_product

Retrieve product details and associated meters from the Pulse billing platform to manage usage-based billing and track metered services.

Instructions

Get details of a specific product including its meters

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": {
      "productId": "<productId>"
    },
    "name": "get_product"
  }
}

Input Schema

Name	Required	Description	Default
`productId`	Yes	The product ID

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 20, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It implies a read operation ('Get details'), but does not specify whether it requires authentication, rate limits, error handling, or what the return format includes beyond 'details' and 'meters'. For a tool with no annotation coverage, this is a significant gap in transparency.

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, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates the tool's function and scope, making it easy to parse and understand quickly. There is no wasted verbiage or structural issues.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete for a tool that retrieves product details. It does not explain what 'details' or 'meters' entail, potential error conditions, or authentication requirements. For a read operation with no structured output information, more context is needed to guide the agent effectively.

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 description coverage is 100%, with the parameter 'productId' fully documented in the input schema. The description does not add any additional meaning or context beyond what the schema provides, such as format examples or constraints. Since the schema handles the parameter documentation adequately, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get details of a specific product including its meters.' It specifies the verb ('Get'), resource ('product'), and scope ('details' and 'meters'). However, it does not explicitly differentiate from sibling tools like 'list_products' or 'get_usage', which slightly limits its clarity in distinguishing use cases.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as needing a product ID, or contrast it with sibling tools like 'list_products' for browsing or 'get_usage' for usage data. This lack of contextual usage information leaves the agent without clear direction.

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