AIVA MCP Server

aiva_get_churn_risk

Identify customers likely to cancel subscriptions by analyzing churn risk scores. Filter by risk level and retrieve prioritized lists for retention actions.

Instructions

Get customers at risk of churning, sorted by risk score.

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": "aiva_get_churn_risk"
  }
}

Input Schema

Name	Required	Description	Default
`riskLevel`	No		high
`limit`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jan 24, 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 mentions sorting by risk score but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, authentication needs, data freshness, or what the output format looks like. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 extremely concise and front-loaded: a single sentence that directly states the tool's purpose. There is no wasted language, and every word earns its place by conveying essential information about what the tool does.

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 retrieving churn risk data, no annotations, and no output schema, the description is incomplete. It doesn't explain the return values, error handling, or behavioral traits like data sources or update frequency. For a tool with significant contextual gaps, this description falls short of providing enough information for effective use.

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 description adds no parameter semantics beyond what the input schema provides. With 0% schema description coverage, the schema itself documents the parameters ('riskLevel' with enum values and default, 'limit' with default), but the description doesn't explain what these parameters mean or how they affect the results. This meets the baseline of 3 since the schema handles parameter documentation adequately.

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 customers at risk of churning, sorted by risk score.' It specifies the verb ('Get'), resource ('customers at risk of churning'), and sorting behavior. However, it doesn't explicitly differentiate from sibling tools like 'aiva_get_customer' or 'aiva_search_customers', which might also retrieve customer data.

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 doesn't mention prerequisites, exclusions, or compare it to sibling tools like 'aiva_get_customer' or 'aiva_search_customers', which could be used for similar customer data retrieval. Usage is implied but not explicitly defined.

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

Deploy Server