get_late_game_sportsSports prediction markets on Polymarket closing within a few hours with a high-certainty leading outcome. Targets near-certain resolution for late-game positioning.
| Name | Required | Description | Default |
|---|---|---|---|
| hours_max | No | Maximum hours until market closes (default: 6h) | |
| certainty_pct | No | Minimum leading outcome probability as percentage, e.g. 85 = 85% (default: 85) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the agent knows the tool is safe and results may change. The description adds context by narrowing the scope to sports markets with high certainty and imminent closure, which is useful beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of two concise sentences that front-load the purpose and add a single clarifying sentence. Every word earns its place with no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple filtered list tool with two optional parameters and no output schema, the description adequately explains what the tool returns and the criteria. No additional information is needed for correct use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptive parameter names and descriptions. The tool description reinforces the concept of 'a few hours' and 'high certainty' but does not add new information beyond what the schema provides. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves sports prediction markets on Polymarket that are closing within a few hours and have a high-certainty leading outcome. This specific verb-resource-scope combination distinguishes it from siblings like 'get_markets_near_resolution' or 'get_odds', which target different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for late-game positioning with near-certain resolution markets. It provides context but lacks explicit when-not-to-use or alternatives. Sibling tools such as 'get_markets_near_resolution' or 'get_odds' are present, so a clearer exclusion would improve this dimension.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools are specialized, but several pairs have fuzzy boundaries: e.g., get_funding_rates vs get_top_funding_rates, get_basic_macro vs get_macro_context, get_simple_iv vs get_options_iv. An agent could easily select the wrong one.
Most tools follow a 'get_X' pattern with descriptive noun phrases. There are a few exceptions like 'create_api_key' and 'search_markets', but overall the convention is consistent and readable.
With 47 tools, the server is overloaded. While the domain is broad, this many tools makes discovery and selection difficult for an agent, reducing coherence.
The tool set covers an impressively wide range: macro data, funding, prediction markets, OI history, whale tracking, risk analytics, position sizing, backtesting, and signal generation. It leaves no obvious gaps for a crypto trading assistant.