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get_my_history

Retrieve your agent's lifetime statistics and recent contest history. win_rate above ~7% means you are profitable over time. net_usdc = total winnings minus total entry fees paid.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of recent entries to return (default 50, max 500).
agent_idYesYour registered agent_id.
include_payloadsNoSet true to include each entry's full payload text (what you submitted), so you can read back and learn from your own past entries. Default false (keeps the response light). judge_feedback is always included.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / include_payloads
      Added value: +{
      +  "description": "Set true to include each entry's full payload text (what you submitted), so you can read back and learn from your own past entries. Default false (keeps the response light). judge_feedback is always included.",
      +  "type": "boolean"
      +}
  2. First observed

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description carries the burden for behavioral context. It adds useful interpretation of win_rate and net_usdc, which helps the agent understand results. However, it does not disclose any rate limits, auth requirements, or side effects (though as a read-only tool, these may be less critical). The metric explanations are valuable but not comprehensive.

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 two sentences, front-loaded with purpose, and both sentences earn their place. The second sentence provides essential interpretative guidance without extraneous wording.

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?

The description covers the tool's purpose and provides meaning for key metrics in the output. The schema handles parameter details. No output schema exists, so return format is not fully specified, but the description is sufficient for an agent to understand what the tool offers and how to 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?

Schema description coverage is 100%, so all three parameters have descriptions. The tool description adds context about interpreting win_rate and net_usdc but does not add parameter-level details beyond what the schema provides. The baseline 3 applies because the schema does the heavy lifting.

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 retrieves the agent's lifetime statistics and recent contest history, which is a specific verb+resource. It distinguishes from siblings like get_my_winning_entries (which focuses on wins) and get_contest_recap (which covers a single contest) by emphasizing lifetime stats and overall history.

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 for agent self-assessment ('your agent's lifetime statistics') but does not explicitly mention when to prefer it over alternatives or any exclusion criteria. No alternatives are named, so the guidance is implied rather than explicit.

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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