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TeleKashOracle

telekash-mcp-server

list_markets

Browse and filter active prediction markets from Kalshi, Polymarket, and Metaculus across 7 categories including sports, crypto, and politics. Sort by trading volume, probability, or closing date to discover betting opportunities.

Instructions

Browse and discover prediction markets across 7 categories with filtering and sorting.

Lists active betting markets from Kalshi, Polymarket, and Metaculus. Filter by category, sort by trading volume, probability, or closing date. 500+ markets available. Categories: sports, crypto, politics, economics, pop_culture, weather, other. Use when exploring what predictions are available, finding trending markets, or discovering betting opportunities.

Example queries:

  • "Show me crypto prediction markets" → Bitcoin, Ethereum, altcoin forecasts

  • "What sports markets are trending?" → NFL, NBA, soccer odds

  • "List political predictions" → elections, legislation, geopolitics

  • "What economic forecasts are available?" → GDP, inflation, interest rates

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFilter by category (default: all)
sort_byNoSort order (default: volume)
limitNoMaximum markets to return (default: 10, max: 50)
sourceNoFilter by data source (default: all)
jurisdictionNoFilter by regulatory jurisdiction. US-regulated = Kalshi (CFTC-regulated), international = Polymarket, forecasting = Metaculus (not gambling). Default: all.

Implementation Reference

  • The implementation of the `list_markets` tool handler.
    async function listMarkets(
      supabase: SupabaseClient,
      args: {
        category?: string;
        sort_by?: string;
        limit?: number;
        source?: string;
        jurisdiction?: string;
      },
    ): Promise<ToolResult> {
      const {
        category = "all",
        sort_by = "volume",
        limit = 10,
        source = "all",
        jurisdiction = "all",
      } = args;
      let effectiveSource = source;
      if (jurisdiction !== "all" && source === "all") {
        const jSources = JURISDICTION_SOURCES[jurisdiction];
        if (jSources?.length === 1) effectiveSource = jSources[0];
      }
      const effectiveLimit = Math.min(Math.max(1, limit), 50);
    
      let query = supabase
        .from("telekash_markets")
        .select(
          "id, external_id, title, category, source, external_odds, raw_data, status, closes_at",
        )
        .eq("status", "active")
        .limit(effectiveLimit);
      if (category !== "all") query = query.eq("category", category);
      if (effectiveSource !== "all") query = query.eq("source", effectiveSource);
    
      switch (sort_by) {
        case "probability":
          query = query.order("external_odds->yes", { ascending: false });
          break;
        case "closing_date":
          query = query.order("closes_at", { ascending: true });
          break;
        default:
          query = query.order("raw_data->volume", {
            ascending: false,
            nullsFirst: false,
          });
      }
    
      const { data, error } = await query;
      if (error) throw new Error(`Database error: ${error.message}`);
    
      // eslint-disable-next-line @typescript-eslint/no-explicit-any
      const markets = (data || []).map((m: any) => {
        const ji = SOURCE_JURISDICTION[m.source] || SOURCE_JURISDICTION.demo;
        return {
          id: m.id,
          title: m.title,
          category: m.category,
          source: m.source,
          jurisdiction: ji.jurisdiction,
          yes_probability: Math.round((m.external_odds?.yes || 0.5) * 100),
          volume_24h: m.raw_data?.volume_24h || m.raw_data?.volume || 0,
          closes_at: m.closes_at,
          status: m.status,
        };
      });
    
      return json({
        markets,
        total: markets.length,
        filters: { category, sort_by, source: effectiveSource, jurisdiction },
      });
    }
Behavior3/5

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

With no annotations provided, the description carries the full burden. It mentions '500+ markets available' (scale), 'active betting markets' (scope), and data sources, but lacks details on rate limits, authentication needs, pagination, error handling, or response format. It adequately describes what the tool does but misses operational constraints.

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 description is well-structured and front-loaded with core functionality, followed by usage guidance and examples. It's appropriately sized but could be slightly more concise by integrating the category list into the first sentence. The example queries are helpful but add length.

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?

For a tool with 5 parameters, no annotations, and no output schema, the description is moderately complete. It covers purpose, usage, and high-level behavior but lacks details on response format, error cases, and operational limits. Given the complexity, it should provide more behavioral context to compensate for missing structured data.

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 100%, so the schema already documents all 5 parameters thoroughly. The description adds minimal value by listing categories and mentioning filtering/sorting in general terms, but doesn't provide additional syntax, format, or interaction details beyond what the schema provides. Baseline 3 is appropriate.

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's purpose with specific verbs ('browse', 'discover', 'lists') and resources ('prediction markets'), and distinguishes it from siblings like 'search_markets' by emphasizing exploration rather than targeted searching. It explicitly mentions the three data sources and 7 categories.

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?

The description provides explicit usage guidance with 'Use when exploring what predictions are available, finding trending markets, or discovering betting opportunities,' which clearly differentiates it from siblings like 'search_markets' (likely for targeted queries) and 'get_trending' (likely for a curated list). The example queries further illustrate appropriate use cases.

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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