Pulse MCP Server

get_usage

Query aggregated usage data from the Pulse billing platform, with filters for product, customer, or date range to analyze consumption patterns.

Instructions

Query aggregated usage data, optionally filtered by product, customer, or date range

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": "get_usage"
  }
}

Input Schema

Name	Required	Description
`productId`	No	Filter by product ID
`customerId`	No	Filter by customer external ID
`startDate`	No	Start date (ISO 8601)
`endDate`	No	End date (ISO 8601)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Feb 20, 2026

TDQS

B3.2/5.0

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 mentions querying aggregated data but fails to specify critical traits such as whether this is a read-only operation, if it requires authentication, rate limits, pagination, or the format of returned data. This leaves significant gaps for an AI agent to understand how the tool behaves.

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 action ('Query aggregated usage data') and succinctly lists optional filters. There is no wasted text, making it highly concise and well-structured for quick comprehension.

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 complexity of a query tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'aggregated usage data' entails, the response format, or any behavioral aspects like error handling. This makes it inadequate for an AI agent to fully understand the tool's context and usage.

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 has 100% description coverage, clearly documenting all four parameters (productId, customerId, startDate, endDate) with their types and purposes. The description adds minimal value beyond this by listing the filter options but doesn't provide additional semantics like examples or constraints, so it meets the baseline for high schema coverage.

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 with the verb 'Query' and resource 'aggregated usage data', making it specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_agent_usage', which might have overlapping functionality, preventing a perfect score.

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

Usage Guidelines3/5

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

The description implies usage by mentioning optional filters (product, customer, date range), suggesting when to apply them, but it lacks explicit guidance on when to use this tool versus alternatives like 'get_agent_usage' or 'track_event'. No exclusions or prerequisites are stated, leaving room for ambiguity.

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