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TeleKashOracle

telekash-mcp-server

search_markets

Search prediction markets across Kalshi, Polymarket, and Metaculus to find active betting opportunities on politics, crypto, sports, economics, and technology topics.

Instructions

Search 500+ prediction markets by keyword, topic, or natural language query.

Full-text search across Kalshi, Polymarket, and Metaculus. Finds markets matching any topic — politics, crypto, sports, economics, entertainment, science, technology, weather. Returns matching active markets sorted by relevance and trading volume. Use when looking for specific predictions, events, or outcomes to bet on.

Example queries:

  • "Trump election 2028" → presidential race odds

  • "Bitcoin price prediction" → BTC price target markets

  • "Super Bowl winner" → NFL championship odds

  • "AI regulation" → technology policy predictions

  • "Fed interest rate" → monetary policy forecasts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (e.g., 'Trump', 'Bitcoin', 'Super Bowl')
limitNoMaximum results to return (default: 10, max: 50)

Implementation Reference

  • The implementation of the `search_markets` tool logic which queries the `telekash_markets` database table using Supabase.
    export async function searchMarkets(
      query: string,
      limit: number = 10,
    ): Promise<Market[]> {
      const db = getDatabase();
    
      const { data, error } = await db
        .from("telekash_markets")
        .select("*")
        .eq("status", "active")
        .or(`title.ilike.%${query}%,description.ilike.%${query}%`)
        .limit(limit);
    
      if (error) {
        throw new Error(`Failed to search markets: ${error.message}`);
      }
    
      return data || [];
    }
  • The registration of the `search_markets` tool in the MCP tool switch-case handler.
    case "search_markets":
      return searchMarkets(supabase, args as { query: string; limit?: number });
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing key behaviors: it searches across three specific platforms, returns active markets sorted by relevance and trading volume, and handles natural language queries. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context.

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?

The description is efficiently structured: first sentence states core function, second expands scope, third explains sorting, fourth gives usage guidance, followed by helpful examples. Every sentence adds value with zero wasted words, and key information is front-loaded.

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

Completeness4/5

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

For a search tool with 2 parameters, 100% schema coverage, and no output schema, the description is quite complete. It explains what platforms are searched, what types of content are covered, how results are sorted, and when to use it. The main gap is lack of output format details, but given the tool's relative simplicity, this is acceptable.

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 baseline is 3. The description adds minimal parameter semantics beyond the schema—it provides example queries that illustrate the 'query' parameter usage but doesn't explain 'limit' beyond what's in the schema. This meets but doesn't exceed baseline expectations.

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 ('search', 'finds', 'returns') and resources ('500+ prediction markets', 'Kalshi, Polymarket, and Metaculus'). It distinguishes from sibling tools like 'list_markets' by specifying full-text search across multiple platforms rather than simple listing.

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?

Explicit guidance is provided: 'Use when looking for specific predictions, events, or outcomes to bet on.' This clearly indicates when to use this tool versus alternatives like 'list_markets' or 'get_trending', which likely serve different purposes.

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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