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YichengYang-Ethan

oracle3

by YichengYang-Ethan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_market

Read-only

Fetch detailed data for a single prediction market: title, best bid and ask, volume, closing date, plus Kalshi rules or Polymarket outcomes and fees.

Instructions

Get one market's details: title, best bid and ask, volume and closing date.

Kalshi results include the resolution rules (rules_primary, rules_secondary);
Polymarket results include the outcomes, token ids, neg-risk flag and the
market's fee schedule. Read the rules of every leg here before trusting a
relation in check_constraint_live. For prices plus fee terms only, use
get_quote; for depth, use get_orderbook. Public data, no API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueYesVenue: 'kalshi' or 'polymarket'.
market_idYesKalshi market ticker (e.g. KXFEDDECISION-28JAN-H0) or Polymarket market id (e.g. 2589812), as returned by search_markets.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.2.2
    • addedInput schema / properties / market_id / description
      Added value: +"Kalshi market ticker (e.g. KXFEDDECISION-28JAN-H0) or Polymarket market id (e.g. 2589812), as returned by search_markets."
    • addedInput schema / properties / venue / description
      Added value: +"Venue: 'kalshi' or 'polymarket'."
  2. First observedv1.2.1

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds value beyond that: it discloses the no-API-key/public-data auth posture, the venue-dependent payload shapes (Kalshi rules fields vs Polymarket outcomes/token ids/neg-risk/fees), and a workflow caveat about trusting relations. It does not cover rate limits or error behavior, but for a single-item read that is a minor gap.

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?

Front-loaded with the core purpose, then venue-specific detail, then exclusions and alternatives. Every sentence carries distinct information; nothing is redundant padding.

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?

Output schema exists, so return values need not be explained, yet the description still surfaces the venue-dependent keys an agent must reason about. Combined with the routing guidance and auth note, an agent has everything needed to select and call it correctly.

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

Parameters4/5

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

Schema coverage is 100%, so both parameters are already documented, making 3 the baseline. The description goes further by explaining what the venue choice actually changes in the response (Kalshi vs Polymarket result shapes), which is meaning the enum label alone does not convey.

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?

States a specific verb (get) and resource (one market's details) and enumerates the returned fields: title, best bid/ask, volume, closing date. It further differentiates behavior by venue, so an agent can distinguish it from get_quote, get_orderbook and search_markets without opening a schema.

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 routes the agent: get_quote for prices plus fee terms, get_orderbook for depth, and check_constraint_live for relations with the prerequisite that rules must be read first. When-to-use and when-to-use-something-else are both stated.

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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