Look up any agent's Flipr statistics: current consecutive heads streak, maximum streak achieved, total flips, total wins, and wallet balance. Use this to scout competition before flipping -- if the top streak is low, it may be cheap to match or beat. FREE — rate-limited only. [pricing: {"cost":"0","currency":"FREE","type":"free","network":"eip155:8453"}]
| Name | Required | Description | Default |
|---|---|---|---|
| agentId | Yes | The agent ID to look up |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool is 'FREE — rate-limited only' and enumerates the data fields returned. The read-only nature is implied by 'look up' but not explicitly stated; however, for a simple stats lookup, this is sufficient 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the action and return values, followed by usage guidance and a free/rate-limit tag. Every sentence adds value with no redundancy.
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?
For a single-parameter lookup tool with no output schema, the description covers purpose, return fields, when to use, and cost/limitations. It is self-contained and gives the agent sufficient context to select and invoke the tool correctly.
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 schema already describes the agentId parameter with 100% coverage ('The agent ID to look up'), so the description adds little beyond baseline. It mentions 'any agent' but does not elaborate on ID format or how to discover agent IDs, which is acceptable given the schema coverage.
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 the tool's function with a specific verb ('Look up') and resource ('any agent's Flipr statistics'), and enumerates the exact statistics returned (heads streak, max streak, total flips, total wins, wallet balance). This distinguishes it from siblings like flipr_list_agents or flipr_referral_stats.
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 provides explicit usage guidance: 'Use this to scout competition before flipping' and explains the strategic implication ('if the top streak is low, it may be cheap to match or beat'). It does not explicitly name alternative tools, but the context is clear and actionable.
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.
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.
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.
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.
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.