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get_resolution

Analyze prediction market resolution by retrieving time urgency, mechanism details, and theta estimates for Kalshi or Polymarket markets.

Instructions

Get resolution intelligence for a market — time urgency, mechanism, theta estimate.

Args: platform: Platform: "kalshi" or "polymarket". market_id: Platform-specific market identifier.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
platformYes
market_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The get_resolution tool implementation, which proxies a GET request to the Rekko API to fetch market resolution intelligence.
    async def get_resolution(platform: str, market_id: str) -> str:
        """Get resolution intelligence for a market — time urgency, mechanism, theta estimate.
    
        Args:
            platform: Platform: "kalshi" or "polymarket".
            market_id: Platform-specific market identifier.
        """
        return await _request("GET", f"/v1/markets/{platform}/{market_id}/resolution")
Behavior3/5

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

With no annotations provided, the description must carry the full burden. It successfully explains what 'resolution intelligence' encompasses (time urgency, mechanism, theta), adding context beyond the schema. However, it omits operational details like caching behavior, rate limits, or whether this requires specific permissions.

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 with the purpose front-loaded in the first sentence, followed by a clear Args section. Every sentence earns its place; there is no redundancy or verbose explanation of return values (appropriately deferred to the output schema).

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?

Given the tool has only 2 primitive parameters and an output schema exists, the description is appropriately complete. It documents both parameters (compensating for schema deficiencies) and previews the key intelligence concepts returned. For this complexity level, no further elaboration is necessary.

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?

The input schema has 0% description coverage, but the description compensates fully via the Args section. It documents both parameters: specifying valid platform values ('kalshi' or 'polymarket') and clarifying that market_id is platform-specific. This provides essential semantic context missing from the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves 'resolution intelligence' and specifies what that entails (time urgency, mechanism, theta estimate). It uses a specific verb-resource pair that distinguishes it from generic market tools like get_market. However, it lacks explicit differentiation from similar sibling 'check_resolutions'.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like check_resolutions or get_analysis. There are no prerequisites, conditions, or workflow contexts mentioned.

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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