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Sharpnel Trading — Dealer Gamma Levels

The in-terminal paper desk's trades

get_desk_record

Pharos, a paper book running inside the terminal, trades the same walls it calls: every trade, wins and losses, delayed ten minutes exactly like the public stream. The desk's fills are simulated on live data and stated before costs. They are not achievable returns and must never be described as trading performance. https://www.sharpnel-trading.com/desk

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tradesNoHow many recent closed trades to list, 0-25. Default 8.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden, and it does so clearly: the fills are simulated on live data, delayed ten minutes, stated before costs, not achievable, and explicitly must never be described as trading performance. This gives the agent important behavioral limitations beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is fairly short and includes important caveats, but it opens with an in-world persona phrase that obscures the tool's purpose. It also repeats simulated/delayed concepts across sentences, and a direct 'Returns recent simulated paper desk trades' lead-in would be clearer and more front-loaded.

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 one-optional-parameter tool with no output schema, the description provides enough context about the nature, delay, cost basis, and non-performance status of the data. It does not explicitly state the output shape, but the schema's 'recent closed trades to list' plus the description's wins/losses phrasing make the expected result reasonably clear.

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 already documents the only parameter fully: how many recent closed trades to list, 0-25, default 8. The description does not add parameter-specific meaning; the phrase 'every trade, wins and losses' is context, not a clarification of the 'trades' parameter.

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

Purpose3/5

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

The description identifies the resource as the paper desk's simulated trades, with wins/losses and a 10-minute delay, but it never uses a direct operation verb such as 'list', 'return', or 'get'; the agent must infer that from the tool name and title. It also does not differentiate this tool from sibling tools like get_graded_record.

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

Usage Guidelines2/5

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

There is a strong disclaimer about not treating the output as trading performance, but no direct statement of when to use this tool versus alternatives. The description implies the tool is for paper-desk simulated fills, but it gives no comparison to siblings or clear selection criteria.

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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