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Glama

position_size

Read-onlyIdempotent

Calculate Fractional-Kelly position size for a binary bet using bankroll, market price, and your fair value estimate, while capping risk and rejecting negative-edge wagers.

Instructions

Fractional-Kelly position size for a binary contract, given your bankroll, the market price, and YOUR fair value estimate. Caps at a fraction of full Kelly and refuses negative-edge bets. Returns its assumptions — subtract quote_cost before trusting the number.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceYes
fair_valueYes
bankroll_usdYes
max_fraction_of_kellyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.18.1

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds genuinely useful behavior beyond that: it caps at a fraction of full Kelly, refuses negative-edge bets, returns its assumptions, and warns that quote_cost must be subtracted. These are non-obvious traits an agent could not infer from the schema or annotations.

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?

Three dense sentences with zero waste, each earning its place: the first states the core purpose, the second discloses capping and refusal behavior, and the third delivers the output caveat. The most important information is 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 simple calculator with no output schema and no parameter descriptions, the description covers inputs, algorithm, refusal behavior, and a return-value caveat. The only notable gap is the exact shape of the returned 'assumptions' and the primary numeric output, which the agent must infer rather than read.

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?

With 0% schema description coverage, the description must carry the semantic burden, and it largely does: it maps bankroll, market price, and the user's fair value estimate to the three required parameters, emphasizing that fair_value is 'YOUR' subjective estimate as opposed to the market price. It does not clarify units or the exact expected range for price/fair_value, and max_fraction_of_kelly is only alluded to via 'Caps at a fraction of full Kelly.'

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 states a specific verb and resource: it computes a 'Fractional-Kelly position size for a binary contract' from three named inputs. It clearly distinguishes itself from sibling list/order/balance tools by being a sizing calculation rather than a position query or order mutation.

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 its role in a pre-trade workflow, especially with 'subtract quote_cost before trusting the number,' which connects it to a sibling tool. However, it never explicitly states when to use this versus alternatives (e.g., 'use before placing an order') or when not to use it, leaving the usage context implicit rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.