Schema | Polymarket MCP Tool

Polymarket MCP Tool

Describes the environment variables required to run the server.

Name	Required	Description	Default
No arguments

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Server capabilities have not been inspected yet.

Functions exposed to the LLM to take actions

Name	Description
prediction_markets_trending_topicsA	Use when analysing a user’s portfolio and you need external sentiment from prediction markets. Retrieves today’s trending Polymarket topics (ordered by 24-hour volume). Pass any slug from this list to `prediction_markets_markets_by_topic` to pull the individual markets for that theme. Returns str A JSON array (string) of topic slugs ordered by 24-hour volume, e.g. '["Trump-Presidency", "Oil-Prices", "Iran"]'.
prediction_markets_markets_by_topicA	Call after you have a Polymarket topic slug (from the user or `prediction_markets_trending_topics`) and need the top markets under it. Parameters topic_slug : str Identifier such as "trump-presidency". limit : int, default 10 Number of markets to return, ranked by 24-hour volume (desc). Returns str JSON array of objects with the schema: [ { "title": "Trump to win 2024?", "volume": 123456.78, "outcomes": [ {"option": "Yes", "probability": 0.42}, {"option": "No", "probability": 0.58} ] }, … ] Parse with `json.loads`.
search_prediction_marketsB	Search prediction markets by any term. Parameters search_term : str Identifier such as "argentina". Returns str JSON array of objects with the schema: [ { "title": "Trump to win 2024?", "volume": 123456.78, "outcomes": [ {"option": "Yes", "probability": 0.42}, {"option": "No", "probability": 0.58} ] }, … ] Parse with `json.loads`.

Interactive templates invoked by user choice

Name	Description
No prompts

Contextual data attached and managed by the client

Name	Description
No resources