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jiroamato

Polymarket MCP Server

by jiroamato
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

get_event

Read-only

Retrieve a complete prediction market event with all its nested markets, including volume, liquidity, and per-market probability data. Use when you need the full topic instead of a single sub-question.

Instructions

Get one event with all of its nested markets.

Use when the user cares about a whole topic (all candidates, all strike brackets, etc.). For one specific sub-question, use get_market.

  • event_id: Numeric string ID (e.g. "36173"). Obtain from search_markets or get_active_markets (returned as id).

Returns the event dict with its full markets[]. Notable fields:

  • volume, volume_24hr, liquidity: USD floats (may be null).

  • active, closed: state flags; closed = all sub-markets resolved.

  • end_date: ISO 8601 UTC; null when sub-markets have staggered dates.

  • markets[].outcome_prices: JSON-encoded probability list; null if untraded.

  • markets[].best_bid, best_ask, last_trade_price: probabilities in [0, 1].

  • markets[].one_day_price_change: 24h absolute change in probability (float).

  • markets[].closed: true once that individual market has resolved.

Does NOT include per-market spread, one_week_price_change, or full market description / slug. Use get_market for those.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
event_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Beyond readOnlyHint, the description details what is returned (markets[] with specific fields) and what is NOT included, providing full transparency.

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?

Well-structured with bullet points for notable fields and limitations; slightly long but each part adds value.

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

Completeness5/5

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

Given the output schema exists, the description covers parameter, usage, and key output fields, making it fully complete for correct invocation.

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?

Despite 0% schema coverage, the description fully explains event_id: type (numeric string), example, and retrieval source, adding essential meaning.

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 clearly states the tool retrieves one event with all nested markets, and contrasts it with get_market for sub-questions, distinguishing it from siblings.

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

Usage Guidelines5/5

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

Explicitly tells when to use this tool vs get_market, and explains how to obtain event_id from search_markets or get_active_markets.

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

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