List all available prediction market datasets with their contents, required plan, date coverage, and columns to help users choose data to browse or download.
Every dataset in the archive (order books, top of book, minute bars, trades, settlements, Polymarket, Chainlink...): what it holds, the plan it needs, days available, and its columns.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already establish read-only and open-world behavior, and the description adds useful context beyond that: each result includes what the dataset holds, its required plan, available days, and columns. It does not mention ordering or pagination, but for a listing tool this is a solid behavioral summary.
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 a single front-loaded sentence that immediately states the scope and then enumerates the useful returned fields. The examples are compact and the sentence has no filler.
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 parameterless listing tool with an output schema and clear read-only annotations, the description is complete enough. It explains what the catalog covers and what information each entry supplies, so an agent can call it confidently.
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?
The tool has zero parameters, so there is no parameter behavior to explain. The baseline for a parameterless tool is 4, and the description appropriately focuses on output content rather than inventing parameter semantics.
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 states a specific resource and scope: every dataset in the archive, including examples and the metadata returned for each. It is clear what the tool does, though it does not explicitly distinguish itself from siblings like dataset_series or list_files.
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?
Usage is implied by the catalog-style description, which suggests it is for discovering datasets and comparing their plans, availability, and columns. However, it gives no explicit when-to-use guidance or alternatives for related tools such as dataset_series or search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.