Track how much money has been recovered through auto-chase reminders. Returns total $ recovered and count of invoices that were overdue, got chased, and then got paid. This is the "proof the platform pays for itself" metric — show users exactly how much money Admin Substitute put back in their pocket.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds that the tool returns recovered amount and count from auto-chase reminders, confirming it is a read-only aggregation with a fixed scope. No contradictions; the description provides useful behavioral context beyond annotations.
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?
Two sentences with no wasted words. The first sentence states the purpose and outputs, the second provides context and a use-case hook. Front-loaded and efficient.
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?
Given the tool's simplicity (no input params, no nested objects) and the existence of an output schema, the description fully explains what the tool returns and why it matters. No gaps remain.
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?
The input schema has no parameters (coverage 100%), so the description need not add parameter details. It implicitly justifies the lack of parameters by making the tool a fixed dashboard metric. Score 4 reflects the baseline for zero-param tools.
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 clearly states what the tool does: 'Track how much money has been recovered through auto-chase reminders.' It specifies the return values (total $ and count) and distinguishes it from sibling tools like invoicing.chase, which triggers chases, and invoicing.generate, which creates invoices.
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 frames the tool as 'the proof the platform pays for itself' metric, implicitly guiding use to demonstrate ROI to users. While it doesn't explicitly state when not to use it or provide direct alternatives, the context is clear enough for an AI agent to infer appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct resource+action naming (e.g., leads.create, jobs.list), but some overlap exists in analytics tools (dashboard, detailed, financials) and workflow automations (process_lead vs google_ads_pipeline). Descriptions clarify purposes, so slight confusion is possible but limited.
All tools follow a consistent `domain.action` pattern (e.g., leads.create, billing.status). No mixing of camelCase or snake_case. Even complex names like lifecycle.assess or scaling.readiness_score adhere to the convention.
95 tools is far beyond typical well-scoped servers (3-15). While the server aims to cover an entire business management platform, this volume is overwhelming for an agent, making selection and memory difficult.
The tool surface is exceptionally comprehensive, covering formation, hiring, compliance, funding, leads, quotes, jobs, invoicing, payments, marketing, analytics, integrations, webhooks, workflows, and more. Almost no obvious gaps in the lifecycle of running a trade business.