calc.engineer

zfs ram

zfs_ram

Calculate recommended RAM and ARC sizing for a ZFS storage pool based on workload type, pool size, deduplication status, and L2ARC cache size. Computes minimum and recommended RAM in gigabytes, ARC target size, and dedup table overhead. Accounts for workload-specific IO patterns: NAS (sequential, 1GB/TB), database (random, 2GB/TB), virtualization (mixed, 1.5GB/TB). Deduplication adds approximately 5GB per TB for the DDT. L2ARC index requires 1GB RAM per 10GB of L2ARC. Essential for TrueNAS, FreeNAS, and custom ZFS server builds.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`workload`	No	Primary workload type: general (mixed), nas (sequential reads/writes), database (random IO), virtualization (mixed random)	general
`pool_size_tb`	Yes	Total usable pool size in terabytes
`dedup_enabled`	No	Whether ZFS deduplication is enabled (significantly increases RAM requirements)
`l2arc_size_gb`	No	Size of L2ARC (read cache SSD) in gigabytes; requires additional RAM for index

Output Schema

TableJSON Schema

Name	Required	Description
`notes`	Yes	Human-readable sizing rationale and recommendations
`min_ram_gb`	Yes	Minimum recommended RAM in gigabytes for stable operation
`arc_target_gb`	Yes	Target ARC (Adaptive Replacement Cache) size in gigabytes
`dedup_table_gb`	Yes	Estimated dedup table (DDT) RAM requirement in gigabytes
`recommended_ram_gb`	Yes	Recommended RAM in gigabytes for optimal performance (2x minimum)

TDQS

A4.6/5.0

Behavior5/5

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

With no annotations, the description fully covers behavioral aspects: it explains the calculation logic for different workloads, dedup overhead, and L2ARC index requirements. No contradictions.

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 informative but slightly verbose. It front-loads the purpose and uses clear language. Could be more concise with bullet points, but still effective.

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?

The description explains what the tool computes (min/recommended RAM, ARC target, dedup overhead) despite no output schema. For a calculator with moderate complexity, this is sufficient.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds significant meaning beyond schema fields like workload-specific IO patterns and formulas (e.g., 'NAS: 1GB/TB', 'dedup adds ~5GB/TB').

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 it calculates RAM and ARC sizing for ZFS storage pools, specifying workload types, dedup, and L2ARC. This distinguishes it from sibling tools like zfs_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 implies use cases: TrueNAS, FreeNAS, custom ZFS servers. It provides context but doesn't explicitly state when not to use or list alternatives. The specificity to ZFS RAM sizing is sufficient for differentiation.

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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