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IBM

MCP Math Server

by IBM

in_range

Check if a number falls within a specified minimum and maximum range, with inclusive boundaries by default.

Instructions

Check if a number is within a specified range (inclusive by default). (Domain: arithmetic, Category: comparison)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYes
min_valYes
max_valYes
inclusiveNo
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the default inclusivity behavior ('inclusive by default'), which is useful context not inferable from the schema alone. However, it doesn't describe error handling (e.g., what happens if min_val > max_val), return format (boolean? string?), or performance characteristics. The description adds some value but leaves significant behavioral gaps.

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 a single, efficient sentence that front-loads the core functionality. Every word earns its place: 'Check' (action), 'number' (subject), 'within a specified range' (purpose), and '(inclusive by default)' (key behavioral detail). The domain/category tags are appended concisely without disrupting the main message.

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

Completeness3/5

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

Given the tool's low complexity (simple range check), no annotations, 0% schema description coverage, and no output schema, the description is minimally adequate. It covers the basic purpose and default behavior but lacks details on parameter semantics, error conditions, return values, and usage distinctions from siblings. For a 4-parameter tool with no structured documentation, it should provide more guidance to be fully helpful.

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?

Schema description coverage is 0%, so the schema provides no parameter documentation. The description mentions 'range (inclusive by default)', which implicitly relates to 'min_val', 'max_val', and 'inclusive' parameters, but doesn't explain 'value' or provide details on parameter interactions, constraints, or examples. It adds marginal semantic context but doesn't fully compensate for the lack of schema descriptions.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Check if a number is within a specified range (inclusive by default).' It includes a specific verb ('Check'), resource ('number'), and scope ('range'), though it doesn't explicitly differentiate from sibling tools like 'clamp' or 'between' that might have similar functionality. The domain/category tags provide additional context but aren't part of the core description.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'clamp' (which restricts values to a range) or 'between' (which might check range membership), nor does it specify any prerequisites, constraints, or typical use cases. The agent must infer usage from the tool name and parameters alone.

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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