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Yardstick ATS

Read an interview plan draft

yardstick_interview_plan_drafts_read
Read-onlyIdempotent

Read one interview-plan draft in full: every stage checkpoint (outcomes, competencies, job skills, interview sequence, questions and directions), stage_versions for source_stage preconditions, status, and source metadata. Question ids are always present (question_N fallback), so edits can target them directly.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draft_idYes
request_idNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral value by disclosing what the read returns (all stage checkpoints, stage_versions, status, source metadata) and the guaranteed presence of question ids, which is useful context beyond the annotations.

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 compact and front-loaded, with two sentences that each earn their place: the first defines the tool's scope and return contents, and the second highlights the question-id invariant for downstream edits. There is no filler or repetition.

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

Completeness4/5

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

Given the tool's simplicity and the readOnly annotations, the description adequately explains what is returned and the id guarantee. The main gap is the lack of any guidance on the optional request_id parameter, but overall the tool is sufficiently specified for an agent to use correctly.

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

Parameters1/5

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

Schema description coverage is 0% and the description does not mention draft_id or request_id at all. An agent gets no guidance on what request_id is for, whether it is required, or how draft_id should be formatted, leaving a significant gap for correctly invoking the tool.

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 a specific verb and resource: 'Read one interview-plan draft in full,' and enumerates the contents (checkpoints, stage_versions, status, source metadata). It also differentiates from sibling list tools by emphasizing 'one draft' versus a collection, making the tool's scope unambiguous.

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 gives clear context for when to use this tool: before edits, because 'Question ids are always present... so edits can target them directly.' It does not explicitly name alternative list or update tools or state when not to use it, but the purpose is clear enough for an agent to route 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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