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yakychan

polymarket-mcp

by yakychan

execute_trade

DestructiveIdempotent

Execute a validated Polymarket quote to open a trade in paper or live mode, with optional automatic stop-loss and slippage protection. Reusing a quote ID returns the identical operation and provides a receipt for confirmation.

Instructions

EJECUTA una cotización vigente: dinero virtual en paper, REAL en live. reason: motivo decidido por la IA. Repetir quote_id devuelve la misma operación. Mostrar el comprobante al usuario; luego consultar poll_updates para confirmaciones. SL optativo: stop_loss_percent=20 activa salida al caer 20% el precio del token desde la entrada. stop_slippage=0.03 permite 3 centavos por debajo del disparador.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonYes
quote_idYes
stop_slippageNo0.03
stop_loss_percentNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior5/5

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

The description adds substantial behavior beyond the annotations: it distinguishes paper trading from live real-money trading, states idempotency ('Repetir quote_id devuelve la misma operación'), reveals the async confirmation flow via poll_updates, and explains stop-loss and slippage behavior. This goes well beyond the readOnly/destructive/idempotent hints.

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 compact and front-loaded with the core action, followed by workflow notes and parameter details. Every sentence adds useful information, and there is no filler or repetition of schema fields.

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 real-money-capable mutation with no output schema, the description covers the essential context: paper vs live mode, idempotency, next steps, and optional risk controls. It does not explicitly describe the return payload, but mentioning the 'comprobante' and poll_updates is enough for an agent to proceed.

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

Parameters5/5

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

Schema description coverage is 0%, yet the description explains all four parameters: reason as the AI-decided motive, quote_id as the identifier that yields the same operation when repeated, stop_loss_percent with a concrete exit example, and stop_slippage with a concrete tolerance example. This fully compensates for the lack of schema-level descriptions.

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

Purpose4/5

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

The description opens with a specific action and resource: 'EJECUTA una cotización vigente', making it clear this tool executes an existing quote rather than creating one. It does not explicitly name the sibling alternative quote_trade, but the wording is specific enough to distinguish execution from quoting.

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 clear operational guidance: use it on a current quote, show the receipt to the user, then consult poll_updates for confirmation. It does not explicitly state when not to use the tool or name alternatives, but the workflow context is sufficient for most agents.

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