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Arjein

Polymarket MCP Server

by Arjein

get_order_book

Retrieve all pending bids and asks for a prediction market outcome token, returning the full order book depth mapped to price levels for analysis.

Instructions

Retrieve the current full depth of the order book (all pending bids and asks) for a specific outcome token.

This provides a complete snapshot of all open buy and sell orders mapped to their respective price levels.

Args: token_id (str): The fundamental token ID associated with a specific outcome (e.g., the YES or NO side of the bet) in a prediction market.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
token_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions 'full depth' and 'complete snapshot of all open buy and sell orders mapped to their respective price levels,' which adds some behavioral context about what's returned. However, it doesn't disclose rate limits, caching, or auth requirements. Since output schema exists, return format is less critical.

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?

Front-loaded with the core action and resource, followed by a concise elaboration and parameter description. No wasted sentences, though the Args section is brief.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema present, the description needn't explain return values, and it does provide parameter semantics. However, as a market data tool with no annotations, more guidance on when to use it vs siblings and any behavioral traits (e.g., data freshness) would improve completeness.

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 0%, but the description provides semantic meaning for token_id: 'The fundamental token ID associated with a specific outcome (e.g., the YES or NO side of the bet) in a prediction market.' This adds context beyond the schema's bare 'Token Id' title, compensating for the lack of schema description.

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?

Clear specific verb+resource: 'Retrieve the current full depth of the order book (all pending bids and asks) for a specific outcome token.' This distinguishes it from sibling get_price and get_order_books (plural), though the description doesn't explicitly differentiate from the plural variant. Purpose is clear but lacks sibling differentiation.

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?

No guidance on when to use this vs alternatives like get_order_books (plural), get_price, or get_midpoint. The description implies it's for a single token's full depth, but doesn't state exclusions or alternatives.

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