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AI infrastructure bottlenecks explained

ai_infrastructure_bottlenecks
Read-onlyIdempotent

Explains the physical constraints on the AI buildout beyond chips: compute, memory (HBM), optics, power, space (sites, backhaul), and servers (racks, cooling). Omit slug for all six. Includes example public companies often cited in discussion; these are not investment picks.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoOptional: one bottleneck.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/non-openWorld, so the safety profile is covered. The description adds genuinely useful behavioral context that annotations cannot: the response includes example public companies, and it pre-empts misuse with "these are not investment picks," managing expectations about output content.

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?

Two dense sentences, front-loaded with the scope enumeration and closed with the disclaimer. Every clause carries information; nothing is padded or repeated from the schema.

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?

For a single optional-enum, read-only explanation tool with no output schema, the description adequately conveys scope, the default behavior, and the nature of the returned content. The absence of any return-structure hint is a minor gap, but the domain framing is sufficient for correct invocation.

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% and the enum values are self-describing, so the baseline is 3. The description adds value beyond the schema by explaining the omitted-slug default (all six categories) and by expanding terse enum labels into parentheticals ("memory (HBM)", "space (sites, backhaul)").

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?

States a specific verb ("Explains") and a precise resource ("physical constraints on the AI buildout beyond chips"), then enumerates the six covered areas (compute, memory/HBM, optics, power, space, servers). An agent immediately knows the content domain, though it never explicitly contrasts itself with the similarly-themed ai_bottleneck_quiz sibling.

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

Usage Guidelines3/5

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

The only usage instruction is "Omit slug for all six," which tells the agent how to invoke the default case. There is no explicit guidance on when to reach for this tool versus alternatives, and no stated exclusions, so usage is implied rather than spelled out.

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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