Retrieve a customer's churn risk score by customer ID. Use it to spot at-risk accounts and prioritize retention actions.
Look up a customer's churn risk score (Project 1: tabular ML model).
Args:
customer_id: Customer id in "CUST-XXXXXX" format, e.g. "CUST-000042".
| Name | Required | Description | Default |
|---|---|---|---|
| customer_id | Yes |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. 'Look up' implies a read-only operation, and naming the model type adds slight context, but it says nothing about what the score means, its range, whether it is cached/stale, latency, or error behavior. For a scoring tool with zero annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the purpose, then the single argument with its format and example. No filler sentences. The 'Args:' block is a reasonable structure for a one-parameter tool, though slightly formal for such a short description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the description is the only place to explain the return value, and it never says what the churn risk score looks like (range, units, interpretation). For a minimal single-parameter lookup it is mostly sufficient to call correctly, but the result is unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the schema only labels the field 'Customer Id', so the description must compensate. It does: it specifies the required format 'CUST-XXXXXX' and supplies a concrete example 'CUST-000042', which is exactly the information needed to form a valid call.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'Look up a customer's churn risk score'. It further identifies the underlying model ('Project 1: tabular ML model'). It does not position itself against siblings, but the siblings (support_ticket_category, policy_question) are unrelated lookups so differentiation is not really needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus the other lookups or any alternative. There are no prerequisites, no exclusions, and no indication of the context in which churn scoring is appropriate (e.g., during retention workflows).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.