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Horse Truth Machine Intelligence

horse_explanation

Read-only

PAID: US$0.02 per successful autonomous x402 call, or 1 Horse Truth direct credit. Return a structured explanation of the current derived Horse Truth read without exposing raw provider records. Use this tool when you want the reasons behind the current read, as a list of factors each with its derived value. Use horse_snapshot instead for the summary values themselves, and horse_changes for what changed over time.

Input Schema

NameRequiredDescriptionDefault
horseYesHorse name as printed in an Australian form guide, e.g. 'Chance With Wolves'. Case and country suffixes like (NZ) are ignored. If unsure of the exact name, call resolve_horse first.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / horse / description
      Added value: +"Horse name as printed in an Australian form guide, e.g. 'Chance With Wolves'. Case and country suffixes like (NZ) are ignored. If unsure of the exact name, call resolve_horse first."
  2. Added

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already establish readOnly/closed-world/non-destructive, but the description adds real behavioral context the annotations cannot: a paid cost model (US$0.02 per successful x402 call or 1 Horse Truth credit) and the guarantee that raw provider records are not exposed. It does not cover rate limits or failure/refund semantics, so it stops short of a 5.

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?

Three sentences, each earning its place, with purpose and the sibling routing front-loaded after a brief cost banner. The pricing sentence leads, which is slightly unusual ordering but compact and information-dense rather than wasteful.

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

Completeness5/5

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

For a single-parameter read tool with full schema coverage, no output schema and clear annotations, the description supplies what is missing: cost, output shape ('list of factors each with its derived value'), and the privacy boundary. An agent can call it correctly without further context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter and schema description coverage is 100%, so the schema already documents the horse-name format, case-insensitivity and the resolve_horse fallback. The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

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

Purpose5/5

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

States a specific verb+resource: returns a structured explanation of the current derived Horse Truth read. It also draws the boundary against siblings by contrasting 'the reasons behind the current read' with the summary values of horse_snapshot and the deltas of horse_changes.

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

Usage Guidelines5/5

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

Explicit when-to-use ('when you want the reasons behind the current read') plus named alternatives for adjacent needs (horse_snapshot for summary values, horse_changes for change over time). Nothing is left to inference about routing.

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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