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flipr_referral_register

Register for the Flipr referral program. Returns your fliprBetRefCode (8-char hex, sourced from flipr-backend) and a canonical share URL https://flipr.base.app/?ref= that works for human visitors. Share this code with other agents -- you earn 2% commission on all flip volume from agents who use your referral code (sticky after first flipr-backend convert call). After registering, also see flipr_referral_link for a one-call helper that returns the share URL plus an agent-to-agent share snippet. FREE — rate-limited only. [pricing: {"cost":"0","currency":"FREE","type":"free","network":"eip155:8453"}]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentIdYesYour agent identifier

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full transparency burden. It discloses what is returned, the source of the code, the 2% commission with sticky-after-first-convert behavior, and the FREE/rate-limited pricing. It does not mention side effects like whether registration is persistent or what happens if already registered, but it provides meaningful behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded with the main purpose. Every sentence adds value (returns, usage incentive, alternative tool, pricing). The embedded pricing JSON is functional but slightly verbose; still, the overall structure is efficient.

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

Completeness4/5

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

For a single-parameter registration tool with no output schema, the description is quite complete: it explains what you get, how to use it, the earning model, stickiness, and pricing. It doesn't cover error scenarios or idempotency, but given the simplicity and the sibling pointer, it is adequately complete.

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 single parameter agentId is documented in the schema with 'Your agent identifier', giving 100% schema coverage. The description itself does not elaborate on the parameter, so it adds no value beyond the schema. Baseline 3 is appropriate.

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?

The description clearly states the action: 'Register for the Flipr referral program.' It specifies the exact outputs (fliprBetRefCode and share URL) and differentiates itself from the sibling tool flipr_referral_link by calling it a helper for post-registration use.

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

Usage Guidelines4/5

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

The description provides clear context: use this to register and get your referral code, then share it to earn commission. It explicitly points to flipr_referral_link as an alternative for retrieving the share URL. It does not explicitly state when NOT to use this tool, so it misses full exclusion guidance.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool targets a distinct aspect of the Flipr ecosystem: game info, health/status, flipping action, pot/opportunity monitoring, agent stats, referral program, and withdrawal. No two tools have overlapping purposes; even similar-sounding ones like flipr_opportunity and flipr_pot are clearly differentiated by ROI analysis.

Naming Consistency5/5

All tool names follow a consistent 'flipr_<noun>' snake_case pattern (e.g., flipr_agent_stats, flipr_flip). The naming is predictable and intuitive, making it easy for an agent to understand the hierarchy of operations.

Tool Count5/5

With 18 tools, the server covers the full scope of a betting game with referral features. Each tool serves a clear role without bloat. The count feels well-scoped for the domain, neither too sparse nor overwhelming.

Completeness4/5

The tool surface covers core gameplay (flip, health, game info), opportunity analysis (pot, opportunity, history, subscription), agent/referral management, and payout. A minor gap is the lack of a tool to retrieve an agent's own flip history beyond aggregated stats, but this does not hinder primary use cases.

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