Step 1 of onboarding a new app onto SparkPay. Recons app_name against existing apps and the rest of the portfolio's plan catalogs, then returns automated findings plus the minimum set of questions that genuinely need a human answer (plan prices, billing model, redirect URLs, trial length, push-webhook opt-in). Pass the answers to create_app_kit next.
| Name | Required | Description | Default |
|---|---|---|---|
| app_name | Yes |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it returns automated findings plus a minimum set of human questions covering prices, billing model, redirect URLs, trial length, and push-webhook opt-in.
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?
The description is three sentences, front-loads the step-1 context, then explains the output and next action. The parenthetical list of question topics is compact and earns its place.
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, but the description explains the return shape at a useful level: automated findings plus targeted human questions. It also explains the next step. It could say more about what findings look like or what happens if the app name conflicts, but the core information is present.
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 description coverage is 0%, so the description must carry parameter meaning. It explains that app_name is reconciled against existing apps and the portfolio's plan catalogs, which adds useful role-level meaning, but it does not specify format, uniqueness expectations, or examples beyond that.
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?
The description states a specific action and resource: Step 1 of onboarding a new app onto SparkPay, reconciling the app name against existing apps and plan catalogs. It clearly distinguishes this planning/reconciliation tool from create_app_kit, which is named as the next step.
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?
It explicitly says this is the first step in onboarding a new app and directs the agent to pass answers to create_app_kit next. That gives both the condition for using this tool and the handoff alternative.
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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