Skip to main content
Glama

generate_execution_plan

Turns human-approved Polymarket trades into a portfolio execution plan, applying Kelly position sizing, risk limits, and correlation checks to define entry sizes.

Instructions

Given a list of human-approved markets with fair values, generate a portfolio execution plan with position sizing (Kelly criterion), risk limits, and correlation checks. Use after reading human approvals from Notion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
total_bankrollNoTotal bankroll in USD
approved_marketsYesMarkets approved by the human for trading
max_portfolio_riskNoMax fraction of bankroll at risk (default 20%)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of disclosing behavior. It reveals that the tool consumes human-approved markets and produces a plan involving Kelly sizing, risk limits, and correlation checks. However, it does not disclose whether the plan is purely returned, persisted somewhere, or triggers any trades, nor whether it actively reads from Notion itself.

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 carry the full message with no filler: the first states the core function and method, the second gives the workflow context. The essential information is front-loaded and every sentence earns its place.

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 tool with only three parameters, a complete schema, and moderate complexity, the description covers the important prerequisites, inputs, and processing approach (Kelly sizing, risk limits, correlation checks). The main gap is the absence of an output schema or explicit description of the returned plan format, but this is a minor omission given how clearly the tool's role is stated.

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 already documents approved_markets, total_bankroll, and max_portfolio_risk with descriptions and defaults. The tool description adds useful high-level framing ('human-approved', 'fair values', 'risk limits') but does not significantly elaborate on individual parameter semantics beyond what the schema provides.

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 action ('generate a portfolio execution plan') on a specific resource ('human-approved markets with fair values'), and includes distinctive details such as Kelly criterion position sizing, risk limits, and correlation checks. This clearly differentiates it from siblings like calculate_trade, which likely targets individual trades rather than a portfolio plan.

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

Usage Guidelines4/5

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

The description gives explicit contextual guidance: 'Use after reading human approvals from Notion,' establishing the prerequisite workflow and when this tool is appropriate. It does not explicitly name alternatives or exclusions, but the temporal cue is strong enough for an agent to decide when to invoke it.

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