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checkyourself

by KyaniteLabs
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Python
Local

Get Schema

schema
Read-onlyIdempotent

Retrieve a named JSON schema from the repository to inspect expected fields before producing or validating artifacts.

Instructions

Return a bundled CheckYourself JSON schema by name so an agent can inspect expected fields before producing or validating artifacts. This reads the repository's schema file and returns it; it does not validate an artifact. Requires no authentication. It reads local inputs only, does not make network calls, does not modify local files, and has no external rate limits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesSchema name to return.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.1.2
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / name / description
      Added value: +"Schema name to return."
    • addedInput schema / properties / name / enum
      Added value: +[
+  "backlog",
+  "capabilities",
+  "challenge",
+  "challenges",
+  "coverage",
+  "dashboard",
+  "dashboard-data",
+  "diff",
+  "learning-plan",
+  "next",
+  "receipt",
+  "report",
+  "scan",
+  "score"
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "description": "The requested bundled JSON schema.",
+  "type": "object"
+}
  2. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description discloses additional behavioral traits: no authentication required, no network calls, no file modification, and no rate limits. It also specifies the data source ('reads the repository's schema file'), which adds context beyond what annotations capture.

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

Conciseness5/5

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

The description is front-loaded with the primary purpose in the first sentence, followed by focused behavioral disclosures. Each sentence carries distinct information, with no redundancy or filler.

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 simple single-parameter tool with an output schema, the description is complete: it explains what is returned, the parameter constraint, the read-only behavior, and the intended use case. Error handling is not described, but the enum-restricted input and presence of an output schema make this sufficient.

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?

The schema already provides 100% coverage with a description ('Schema name to return') and a full enum of valid values, so the tool-level description adds no additional parameter semantics. Per the rubric, with high schema coverage the baseline score of 3 is appropriate.

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?

The description states the primary function with a specific verb ('Return a bundled CheckYourself JSON schema') and a clear purpose ('so an agent can inspect expected fields before producing or validating artifacts'). It also distinguishes itself from validation-focused siblings by explicitly noting 'it does not validate an artifact,' which differentiates it from tools like validate and coverage_check.

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

Usage Guidelines4/5

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

The description provides explicit when-to-use guidance ('before producing or validating artifacts') and a clear when-not-to-use constraint ('does not validate an artifact'). It doesn't name alternative sibling tools explicitly, but the contrast with the validation workflow is clear enough to route an agent correctly.

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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