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akari-x402

by io.github.edvyno
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akari_inplay_tickets

Polymarket in-play ticket dataset - every settled in-play bet this desk placed: fixture, minute, book price at entry, our model probability, edge, stake, settled PnL and the rule version that fired it. Every settled loss is in there, plus the rows where our model scored worse than the market. The same tape that produced our published v4.1 rule. Free sample at /x402/dataset/preview.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "akari_inplay_tickets"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that every settled loss is included and that rows where the model underperformed the market are present, plus provenance to the v4.1 rule, which adds meaningful transparency. However, it omits access details such as whether the full dataset is free or paid, rate limits, authentication, and response format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the dataset scope and fields, then adds relevant completeness and provenance notes. It is fairly concise, though the reference to the published v4.1 rule and the preview endpoint has a slightly promotional tone rather than purely operational structure.

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

Completeness3/5

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

For a zero-parameter dataset with no output schema, the description does a reasonable job listing returned fields and noting the inclusion of losses and model-worse rows. It still lacks operational context such as whether the full dataset is paid via x402, how the response is structured, and when this is preferable to related sibling tools.

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 tool has zero input parameters, so there are no parameter semantics to document. The baseline for a parameterless tool is 4, and the description does not need to compensate for missing schema coverage.

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 identifies the resource as a Polymarket in-play ticket dataset and lists the concrete fields returned: fixture, minute, entry price, model probability, edge, stake, settled PnL, and rule version. It is specific enough for an agent to know this retrieves settled in-play betting records, though it does not explicitly differentiate itself from the related sibling akari_inplay_edge.

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 says what the dataset contains but gives no explicit guidance on when to use this tool versus alternatives such as akari_inplay_edge or akari_picks. A preview endpoint is mentioned, but there are no conditions, exclusions, or decision rules for selecting this tool.

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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