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AimYear

by io.github.Yahhi
OverviewDiscussionsAdmin

Check goal draft

check_goal_draft
Read-onlyIdempotent

Check a goal that is not saved yet against the rubric (a countable metric with value today and target, or a done-or-not result you can check yes or no; deadline within 12 months; pace per week). Changes nothing. Returns problems[] and next_question: loop until problems is empty, then call set_goal. Prefer count; use done only when no honest number exists.

Input Schema

NameRequiredDescriptionDefault
kindNocount (default): a number from baseline to target, e.g. "15 paying customers". done: a one-time result checked yes or no, e.g. "launch v2 in the stores"; no metric, baseline or target, progress is the share of stages done
titleNo
metricNothe one number and its unit, e.g. "paying customers"
targetNo
baselineNovalue today
deadlineNoYYYY-MM-DD in the user's timezone

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / kind
      Added value: +{
+  "description": "count (default): a number from baseline to target, e.g. \"15 paying customers\". done: a one-time result checked yes or no, e.g. \"launch v2 in the stores\"; no metric, baseline or target, progress is the share of stages done",
+  "enum": [
+    "count",
+    "done"
+  ],
+  "type": "string"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly/idempotent/non-destructive), so 'Changes nothing' is largely reinforcing structured data. However, the description goes beyond annotations by disclosing the return contract ('Returns problems[] and next_question') and the iteration semantics of the loop-until-clean workflow, which no output schema supplies.

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?

Front-loaded with the core action and free of filler; every sentence carries purpose, workflow, or parameter guidance. The packed parenthetical rubric and semicolon chains make it dense to parse, which is the only thing keeping it from a 5.

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?

No output schema exists, yet the description supplies the return shape (problems[], next_question), the validation criteria, and the downstream step (set_goal). For a validation tool with six params, nothing an agent needs to call it correctly is missing.

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?

Schema coverage is 67%, and the description adds meaning past the schema: it maps metric/baseline/target onto the rubric ('a countable metric with value today and target'), states the 'deadline within 12 months' validation rule, and explains the count-vs-done tradeoff that the enum alone doesn't resolve.

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 and resource — 'Check a goal that is not saved yet against the rubric' — and pins down scope (draft, unsaved) that distinguishes it from get_goal and set_goal. An agent can tell exactly which object this operates on without opening the schema.

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?

Explicitly routes the agent: 'loop until problems is empty, then call set_goal' names the alternative and the condition that selects it. The tie-breaker 'Prefer count; use done only when no honest number exists' resolves the enum choice in ambiguous cases.

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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