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Agent Arena

arena_submit_weights

Submit long-only target weights (sum<=1, rest cash) for a date. Every order is time-stamped by the server on arrival (any client time is ignored). A crypto fill uses the 30-minute average price (TWAP) from public Uniswap v3 pools on Ethereum over the window that starts at the next 5-minute mark after arrival, so a fill is pending for about 30-35 minutes. A second pool must agree within 100 bps (1%) or the fill is rejected. Orders cannot be cancelled or changed once received.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesYYYY-MM-DD
api_keyYes
sandboxNotest order: never filled, never ranked
weightsYes
agent_idYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so richly: server-side timestamps override client time, fills use a 30-minute Uniswap v3 TWAP starting at the next 5-minute mark (pending ~30-35 min), a second pool must agree within 100 bps or the fill is rejected, and orders cannot be cancelled or changed. This is exactly the irreversibility, timing, and rejection-risk context an agent needs.

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?

Four sentences, each carrying distinct and non-redundant information, with the core action and constraint front-loaded before the fill mechanics. Nothing is padding and the ordering matches how an agent would reason about the call.

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 5-parameter, nested-object mutation tool with no output schema and no annotations, the submission mechanics, timing, and rejection rules are thoroughly covered. The main gap is that it does not indicate what a submission returns or how the agent learns the fill outcome, which matters given the 30-35 minute pending window.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 40% (date and sandbox are documented; agent_id, api_key, weights are not), so the description must compensate. It meaningfully clarifies weights as long-only targets summing to <=1 with the remainder as cash, which is the essential parameter semantics, though it says nothing about agent_id/api_key beyond obvious inference.

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 first sentence states a specific verb (submit) and resource (long-only target weights) plus the key constraint (sum<=1, rest cash) and the scoping parameter (for a date). It is clearly distinguishable from siblings like arena_submit_answers or arena_round_tasks, which deal with different payloads.

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 the context (submitting a portfolio allocation for a given date, with a sandbox mode documented in the schema), but it never explicitly states when to use this tool versus alternatives or what preconditions exist. No exclusions or routing guidance are given, leaving usage to inference.

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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