Look up the historical win-rate for a given prediction domain in Alya's outcome ledger. Returns resolved count, wins, losses, win_rate, and last_resolved_at.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain prefix, e.g. 'freelance', 'prediction-market', 'equities'. |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the operation returns specific metrics and appears to be a read-only lookup. However, it does not mention authentication needs, rate limits, or any side effects. The lack of behavioral detail beyond the return fields makes it adequate but minimal.
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 is two sentences: the first states the purpose, the second lists the return fields. No extraneous words, highly efficient and front-loaded.
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?
Given the tool's simplicity (one parameter, no output schema, no nested objects), the description adequately explains its output and purpose. However, it could optionally mention data freshness or scope limitations, which would make it fully complete for a production tool.
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 one parameter 'domain' described with an example in the schema. The description adds context by calling it a 'Domain prefix' and tying it to 'Alya's outcome ledger', slightly enhancing the schema's meaning. Since schema already documents the parameter well, this is a baseline 3.
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 action 'Look up' and the resource 'historical win-rate for a given prediction domain in Alya's outcome ledger', specifying the exact returned fields. This distinguishes it from sibling tools like alya_seismic_forecast or alya_demands_trending, which likely deal with other prediction types.
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 that the tool is for looking up historical accuracy, but it does not explicitly state when to use it over alternatives or when not to use it. No exclusions or alternative suggestions are provided.
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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