Polymarket MCP Server

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place_order

Submit a limit order to buy or sell outcome shares on Polymarket prediction markets, with max order size validation and dry-run testing.

Instructions

Place a limit order on a Polymarket prediction market.

⚠️ WARNING: This action actively spends real currency! The total order value (price × size) is validated against the POLYMARKET_MAX_ORDER_SIZE prior to API submission. To test safely without financial execution, configure POLYMARKET_DRY_RUN=true in the environment .env file.

Args: token_id (str): The fundamental CLOB token ID indicating the precise outcome side (e.g., YES or NO). price (float): The designated limit price, scaling strictly between 0.01 and 0.99 (representing outcome probability). size (float): The aggregate number of shares to purchase or sell. side (str): The functional trading direction, strictly either 'BUY' or 'SELL'. order_type (Optional[str]): The operational order type payload. Options: GTC (Good-Til-Cancelled), FOK (Fill-Or-Kill), GTD (Good-Til-Date), FAK (Fill-And-Kill). tick_size (Optional[str]): The calculated market tick size structure (e.g., '0.1', '0.01', '0.001', '0.0001'). neg_risk (Optional[bool]): Designates whether the overarching market utilizes a negative risk framework.

Input Schema

TableJSON Schema

Name	Required	Default
`side`	Yes
`size`	Yes
`price`	Yes
`neg_risk`	No
`token_id`	Yes
`tick_size`	No	0.01
`order_type`	No	GTC

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 13, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and does disclose the critical trait: this is a real-money mutation with a pre-submission size validation and a dry-run escape hatch. It does not state auth prerequisites, reversibility/cancellability of a placed order, or failure behavior, leaving some behavioral gaps.

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

Conciseness4/5

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

The warning is front-loaded ahead of the Args block, which is the right priority ordering. Phrasing is somewhat inflated ("The functional trading direction", "The fundamental CLOB token ID") but no sentence is truly wasted.

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?

An output schema exists, so return values need not be described, and the high-stakes money-spend risk plus all params are covered. For a 7-param mutation tool with no annotations, only missing prerequisites (auth, reversibility) keep it from being fully complete.

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%, so the description must compensate, and it does: every one of the 7 params is documented with type and meaning (price bounded 0.01-0.99 as probability, side BUY/SELL, order_type GTC/FOK/GTD/FAK, tick_size examples, neg_risk). This adds substantially more meaning than the bare schema titles.

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?

States a specific verb and resource ("Place a limit order on a Polymarket prediction market") and even pins the order type as a limit order. There is no overlapping sibling (the others are read/cancel tools), so differentiation is effectively automatic, though the description never explicitly routes against alternatives.

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?

Gives strong operating context: it spends real currency, order value is validated against POLYMARKET_MAX_ORDER_SIZE, and POLYMARKET_DRY_RUN=true enables safe testing. It lacks explicit when-not conditions or named alternatives (e.g. checking balance/order book first, using cancel_order to reverse), so it stops short of full alternative guidance.

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