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build_propose_resolution_transaction

Build a transaction for a market creator to propose a Yes/No outcome as the resolution for a Solana prediction market.

Instructions

[REQUIRES BAOZI_LIVE=1] Build transaction for creator to propose market outcome.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
marketYesMarket public key
outcomeYesProposed outcome (true=Yes, false=No)
proposer_walletYesProposer wallet (creator)
Behavior2/5

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

There are no annotations, so the description bears full responsibility for behavioral disclosure. It states that the tool 'build[s] transaction' but does not explain whether the transaction is signed, unsigned, or what side effects (if any) this action has. It also does not mention the return value or whether this is a read-only operation. The only behavioral context is the BAOZI_LIVE requirement, which is insufficient for a transaction-building tool.

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 a single, focused sentence with the critical prerequisite placed at the front. It contains no redundant words or fluff. It is efficient, though it could benefit from a brief mention of the transaction's purpose in the broader workflow.

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

Completeness3/5

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

For a tool with three straightforward parameters and no output schema, the description provides the minimal necessary context: what it does and a prerequisite. However, it does not explain the transaction's role in the resolution lifecycle (e.g., that it is the first step before resolving or finalizing) or what the transaction object looks like. This is sufficient for a simple tool but lacks depth.

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 input schema covers all three parameters (market, outcome, proposer_wallet) with clear descriptions, achieving 100% schema description coverage. The tool description adds minimal semantic value, only reinforcing that the proposer is the creator (matching proposer_wallet). Since the schema already explains parameters thoroughly, a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Build transaction for creator to propose market outcome.' It specifies a concrete action (build transaction), an actor (creator), and a purpose (propose market outcome). This distinguishes it from siblings like build_resolve_market_transaction or build_finalize_resolution_transaction, but it doesn't explicitly exclude race-market variants (e.g., build_propose_race_resolution_transaction), so it's not a full 5.

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

Usage Guidelines3/5

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

The description gives a key prerequisite ('[REQUIRES BAOZI_LIVE=1]') and implies the use case (proposing a market outcome). However, it does not explicitly state when to use this tool versus alternatives like build_resolve_market_transaction or build_finalize_resolution_transaction, nor does it mention when not to use it. This is implied usage rather than explicit 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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