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alpha_get_live_markets

Fetch live prediction markets from Alpha Arcade on Algorand to view market summaries including prices, volume, and trading IDs for multi-choice options.

Instructions

Fetch all live Alpha Arcade prediction markets. Returns summary: id, title, marketAppId, prices, volume. Multi-choice markets have an options[] array — use options[].marketAppId for trading.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
networkNoAlgorand network to use (default: mainnet)
itemsPerPageNoNumber of items per page for paginated responses (default: 10)
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses that the tool fetches data (implied read-only) and describes return format details like the options array for multi-choice markets. However, it doesn't mention pagination behavior (implied by itemsPerPage parameter but not explained), rate limits, authentication needs, or error conditions.

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?

Two sentences with zero waste. The first sentence states purpose and return format. The second provides crucial behavioral detail about multi-choice markets. Every word earns its place, and information is front-loaded appropriately.

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 read-only tool with 2 optional parameters and no output schema, the description is reasonably complete. It explains what data is returned and includes important behavioral details about market structure. However, it doesn't address pagination behavior (implied but not explained) or potential limitations, leaving minor gaps.

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?

Schema description coverage is 100%, so the schema fully documents both parameters. The description adds no parameter-specific information beyond what's in the schema. This meets the baseline of 3 since the schema does all the work, but the description doesn't compensate with additional context about parameter interactions or effects.

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 specific action ('Fetch all live Alpha Arcade prediction markets') and resource ('prediction markets'), distinguishing it from siblings like alpha_get_market (single market) and alpha_get_reward_markets (different market type). It explicitly mentions what data is returned, making the purpose unambiguous.

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 implies usage context by specifying it returns 'live' markets and summary data, suggesting it's for overview rather than detailed trading. However, it doesn't explicitly state when to use this versus alternatives like alpha_get_market or alpha_get_reward_markets, nor does it mention prerequisites or exclusions.

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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