calc.engineer

network bandwidth

network_bandwidth

Plan and validate network link capacity for homelab workloads. Determine whether a 1G, 2.5G, 10G, or faster link can handle your concurrent streams (VMs, backups, media, iSCSI). Calculates effective bandwidth after TCP/IP overhead, total required bandwidth, utilization percentage, and remaining headroom. Flags saturation at 80% utilization and identifies whether the bottleneck is network or storage. Provides a 1TB transfer time reference and recommends the next link speed upgrade if your current link is saturated. Useful for planning NIC upgrades, switch purchases, and storage network design.

Input Schema

TableJSON Schema

Name	Required	Description
`link_speed_gbps`	Yes	Network link speed in gigabits per second (e.g. 1, 2.5, 5, 10, 25, 40, 100)
`concurrent_streams`	No	Number of concurrent data streams / transfers
`protocol_overhead_pct`	No	TCP/IP protocol overhead as a percentage (typically 3-5% for TCP, ~1% for UDP)
`stream_bandwidth_mbps`	No	Bandwidth required per stream in megabits per second
`storage_throughput_mbps`	No	Maximum storage read/write throughput in megabits per second. If provided, checks whether storage is the bottleneck

Output Schema

TableJSON Schema

Name	Required	Description
`saturated`	Yes	True if utilization exceeds 80% — link is effectively saturated
`bottleneck`	Yes	Identified bottleneck: 'storage', 'network', or 'none'
`headroom_mbps`	Yes	Remaining bandwidth after all streams (negative means oversubscribed)
`utilization_pct`	Yes	Percentage of effective bandwidth used by all streams
`required_bandwidth_mbps`	Yes	Total bandwidth required by all streams in Mbps
`effective_bandwidth_mbps`	Yes	Usable bandwidth after protocol overhead in Mbps
`time_to_transfer_1tb_hours`	Yes	Time to transfer 1 TB over the effective bandwidth, in hours
`recommended_link_speed_gbps`	Yes	Suggested next link speed upgrade if saturated, null if current link is sufficient

TDQS

A4.6/5.0

Behavior5/5

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

No annotations are provided, so the description carries full burden. It exhaustively discloses behaviors: calculates effective bandwidth after TCP/IP overhead, total required bandwidth, utilization percentage, remaining headroom, flags saturation at 80%, identifies network vs. storage bottleneck, provides 1TB transfer reference, and recommends next upgrade. This is highly transparent.

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 concise paragraph that front-loads the purpose and follows with key details. Every sentence adds value, though a slightly more structured format (e.g., bullet points) could improve readability. Still, it is efficient for an AI agent.

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 has 5 parameters and an output schema (not shown), the description covers all essential aspects: what inputs are used, what calculations are performed, and what outputs are provided (e.g., utilization, headroom, bottleneck detection). It leaves no major gaps for an agent to infer.

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 description coverage is 100% (all parameters have descriptions). The tool description adds context beyond the schema, such as explaining that utilization flags at 80% and that the tool recommends upgrades. This enhances understanding of parameter roles, warranting a score above baseline.

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: 'Plan and validate network link capacity for homelab workloads.' It specifies the verb (plan/validate) and resource (network link capacity), and distinguishes itself from sibling calculators by focusing on network throughput planning.

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 explains when to use the tool (e.g., 'Determine whether a 1G, 2.5G, 10G, or faster link can handle your concurrent streams'). While it does not explicitly state when not to use it or name alternatives, the context is clear enough for an AI agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All calc.engineer tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?