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Get who an error hit and what they pay

get_error_affected_users
Read-only

Retrieve paying customers affected by a specific error, sorted by revenue at risk. Returns user identifiers, monthly revenue, and last hit time for each customer.

Instructions

For one error (by fingerprint), get the PAYING users it actually hit — the row-level moat answer: which paying customer broke, and how much are they worth. Returns a list sorted by revenue at risk (highest first), each row with your own user id, monthly revenue in cents, and when they last hit the error. Joins the error layer to identity and revenue. Returns only your own identifiers — never emails or names you didn't provide. Use when you need the actual customers, not just the counts from get_error_impact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax users to return (1–100, default 25), highest revenue-at-risk first.
projectNoThe app to read, as a project id from list_projects (e.g. 'proj_3a8f137bccdd4f'). With a workspace key (cd_wk_) this is required unless you've set a default via use_project; with a single-app key it is ignored.
fingerprintYesThe error's fingerprint / issue id (e.g. 'a1b2c3').
Behavior4/5

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

Description adds behavioral context beyond the readOnlyHint annotation, detailing joins across layers and sorting by revenue. No contradictions with annotations.

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?

Four sentences pack all essential information without fluff, front-loaded with the core purpose.

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

Completeness5/5

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

Given no output schema, description adequately describes return structure (user id, revenue, last hit time) and behavior. Complete for a 3-param read-only tool.

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 coverage is 100%, but description adds useful context: limit default, project parameter usage conditions, and that results are sorted by revenue. Adds value beyond schema.

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?

Description clearly states the tool retrieves paying users affected by a specific error, sorted by revenue, with explicit distinction from sibling get_error_impact which only provides counts.

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

Usage Guidelines5/5

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

Explicitly states when to use ('when you need the actual customers, not just the counts from get_error_impact') and clarifies identifier handling.

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

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