Moltline Educator Toolkit

Grade Curve

grade_curve

Read-onlyIdempotent

Summarize how an assessment landed: stats, bands, and outliers. FREE.

Reports mean, median, std dev, quartiles, distribution by percentage band, and flagged low outliers. Typical input {"scores": [88, 92, 75, 61, 84], "max_score": 100} returns {"n": 5, "mean": 80.0, "median": 84.0, "std_dev": ..., "q1": ..., "q3": ..., "distribution": {"90-100%": 1, ...}, "outliers_low": [...], "reading": "..."}.

Use on a list of scores already earned. Not for the readability of the assessment itself (readability_report). Errors: on invalid, missing, or malformed input this tool never raises a protocol error; it returns {"error": ""} (for example {"error": "no scores"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "scores": "<scores>"
    },
    "name": "grade_curve"
  }
}

Input Schema

Name	Required	Description	Default
`scores`	Yes	Raw scores, one per student; at least 1 value, e.g. [88, 92, 75].
`max_score`	No	Maximum possible score; must be greater than 0. Default 100.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 17, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is partly redundant here. The description still adds real value with its error contract: it never raises a protocol error, returns {"error": "..."} with remediation hints, and is safe to retry after correction. Only the repeated read-only/idempotent claim duplicates structured data.

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 purpose and cost ("FREE") are front-loaded, then outputs, then an example, then exclusions and error behavior. The inline JSON example is long but earns its place by illustrating both input and return shape; a tighter description could trim some of it.

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 two-parameter read-only computation with an output schema already present, this covers everything: what it computes, example input/output, when not to use it, and exactly how failures surface. An agent could call it correctly on the first attempt.

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 100%, so baseline is 3. The description goes beyond the schema by showing a concrete call with {"scores": [...], "max_score": 100} and the resulting payload, which clarifies how max_score scales the percentage bands and what the scores array represents in practice.

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 ("summarize") and resource (an assessment's scores), then enumerates the exact outputs produced: mean, median, std dev, quartiles, bands, outliers. It explicitly separates itself from readability_report, so an agent can pick it without opening either 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?

"Use on a list of scores already earned" gives the positive condition, and "Not for the readability of the assessment itself (readability_report)" gives the exclusion plus the named alternative. Nothing is left to inference about when to select this tool over a sibling.

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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