calc.engineer

zfs capacity

zfs_capacity

Calculate usable ZFS pool capacity for any RAID level including stripe, mirror, raidz1, raidz2, and raidz3. Computes raw capacity, parity overhead, data disk count, usable terabytes after ZFS metadata overhead (checksums, block pointers, uberblocks), and storage efficiency percentage. Essential for planning NAS builds, TrueNAS/ZFS server storage, and estimating how much usable space a given disk configuration will provide. Supports variable disk sizes and configurable metadata overhead.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`raid_type`	No	ZFS RAID level: stripe (no redundancy), mirror (2-way), raidz1/2/3 (single/double/triple parity)	raidz1
`disk_count`	Yes	Total number of physical disks in the pool
`disk_size_tb`	Yes	Size of each individual disk in terabytes
`record_size_kb`	No	ZFS record size in kilobytes, affects compression and performance
`metadata_overhead_pct`	No	Percentage of raw capacity consumed by ZFS metadata, checksums, and internal structures

Output Schema

TableJSON Schema

Name	Required	Description
`raw_tb`	Yes	Total raw capacity across all disks in terabytes
`usable_tb`	Yes	Usable capacity after parity and metadata overhead in terabytes
`data_disks`	Yes	Number of disks (or disk-equivalents) available for data storage
`parity_disks`	Yes	Number of disks (or disk-equivalents) consumed by parity/mirroring
`efficiency_pct`	Yes	Storage efficiency as a percentage of raw capacity

TDQS

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It describes inputs and outputs, including 'raw capacity, parity overhead...', and notes support for variable disk sizes and configurable overhead. As a calculation tool, this adequately discloses behavior.

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 description is a single paragraph, front-loaded with the main purpose, and each sentence adds value. It is efficient but not extremely terse; could be slightly shorter.

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?

Given the tool is a calculator with output schema present, the description covers all necessary aspects: supported RAID levels, outputs, and usage context. It is complete for the tool's complexity and domain.

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 coverage is 100%, and description adds minimal extra meaning beyond schema. For example, the record_size_kb description in schema matches the one in the tool description. Baseline 3 applies because schema already documents parameters well.

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 clearly states the tool's purpose: 'Calculate usable ZFS pool capacity for any RAID level...' and lists specific RAID types. It distinguishes from siblings like 'raid_iops' by focusing on capacity.

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 usage context: 'Essential for planning NAS builds, TrueNAS/ZFS server storage, and estimating usable space.' It does not explicitly exclude scenarios or mention alternatives, but the context is clear.

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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TDQS

A3.9/5.0

Disambiguation4/5

Despite 89 tools, each has a clearly distinct purpose with detailed descriptions that often reference related tools. Overlap exists (e.g., multiple LoRa/RF tools), but the descriptions are sufficient to distinguish them. Some confusion possible among similar-sounding tools like attenuator_pi and attenuator_tee, but the descriptions explicitly compare them.

Naming Consistency4/5

Consistent underscore-separated lowercase naming. Most tools follow a verb_noun pattern (e.g., capacitor_charge, wire_gauge) or noun_noun (power_cost). Minor inconsistencies such as 'bmi_calculator' vs 'solar_sizing' but overall predictable.

Tool Count2/5

89 tools is far too many for a single MCP server. This scope is more appropriate for multiple specialized servers. The sheer number will slow agent selection and increase cognitive load, reducing coherence.

Completeness3/5

Covers many domains (RF, solar, PCB, networking, math, etc.) but lacks depth in some areas (e.g., no three-phase power, no airflow calculations). Some domains have comprehensive coverage (LoRa/Meshtastic), but others feel incomplete for the tool count.

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