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flipr_bridge_status

Poll status of a Solana payment bridge after a 202 response from flipr_flip. Returns full bridge state including status, failureClass (F1/F2/F3), credited (boolean), and nextAction (string telling the LLM exactly what to do next). Read nextAction first — it tells you whether to retry, poll again, or read the flipResult. Accepts payment on Base mainnet OR Solana mainnet (auto-bridged). See http://localhost:4402/integration#solana for details. [paymentNetworks: ["base","solana"]] FREE — rate-limited only. [pricing: {"cost":"0","currency":"FREE","type":"free","network":"eip155:8453"}]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bridgeIdYesSolana payment signature (base58, ~88 chars) returned in the 202 response from a Solana-paid flip

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and excels: it implies a read-only operation via 'Poll status', enumerates return fields (status, failureClass, credited, nextAction), and discloses payment network support, rate limiting, and free pricing. The nextAction instruction adds behavioral depth by telling the agent exactly how to proceed.

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?

The description is front-loaded with the core purpose, then efficiently covers return fields, usage instructions, payment networks, documentation link, and pricing. Every sentence contributes unique, non-redundant information, and the structure is logical and scannable.

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 the tool's simplicity (one parameter, no output schema), the description is complete: it explains the trigger condition, return values, nextAction semantics, and relevant constraints (payments, rate limiting). It provides an agent with everything needed to invoke and interpret results.

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 schema already covers the single parameter bridgeId comprehensively, including its format ('base58, ~88 chars') and origin ('returned in the 202 response'). The description reinforces this context but adds no new parameter-specific information beyond that, so the baseline score of 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 tool's purpose with a specific verb ('Poll status') and resource ('Solana payment bridge'). It distinguishes itself from siblings by referencing the 202 response from flipr_flip and focusing on bridge state, making its role unambiguous.

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 explicitly specifies when to use the tool ('after a 202 response from flipr_flip') and provides actionable guidance on interpreting nextAction to decide next steps. It does not explicitly name alternative tools or when-not-to-use, but the workflow context is clear enough.

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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